Wearable monitor

ABSTRACT

The present disclosure relates to a wearable monitor device and methods and systems for using such a device. In certain embodiments, the wearable monitor records cardiac data from a mammal and extracts particular features of interest. These features are then transmitted and used to provide health-related information about the mammal.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.17/078912 filed on Oct. 23, 2020, entitled WEARABLE MONITOR which is acontinuation of U.S. application Ser. No. 16/889541 filed on Jun. 1,2020, entitled WEARABLE MONITOR which is a continuation of U.S.application Ser. No. 16/422224, filed May 24, 2019, now U.S. Pat. No.10,667,712 entitled WEARABLE MONITOR, which is a continuation of U.S.application Ser. No. 16/160173, filed Oct. 15, 2018, now U.S. Pat. No.10,299,691 entitled WEARABLE MONITOR WITH ARRHYTHMIA BURDEN EVALUATION,which is a continuation of U.S. application Ser. No. 15/966,258, filedApr. 30, 2018, now U.S. Pat. No. 10,098,559, entitled WEARABLE MONITORWITH ARRHYTHMIA BURDEN EVALUATION, which is a continuation of U.S.application Ser. No. 15/463,944, filed Mar. 20, 2017, now U.S. Pat. No.9,955,887 entitled WEARABLE MONITOR, which is a continuation of U.S.application Ser. No. 14/929,121 filed Oct. 30, 2015,now U.S. Pat. No.9,597,004 entitled WEARABLE MONITOR, which claims the benefit of U.S.Provisional Application No. 62/073,910, filed Oct. 31, 2014, entitledWIRELESS PHYSIOLOGICAL MONITORING. The content of the aforementionedapplications is hereby incorporated by reference in their entireties asif fully set forth herein. The benefit of priority to the foregoingapplications is claimed under the appropriate legal basis, including,without limitation, under 35 U.S.C. § 119(e).

BACKGROUND

For purposes of this disclosure, certain aspects, advantages, and novelfeatures of various embodiments are described herein. It is to beunderstood that not necessarily all such advantages may be achieved inaccordance with any particular embodiment. Thus, various embodiments maybe or carried out in a manner that achieves one advantage or group ofadvantages as taught herein without necessarily achieving otheradvantages as may be taught or suggested herein.

Field of the Invention

System for inferring cardiac rhythm information from heart beat timeseries information collected by wearable sensors, and system forselective transmission of electrocardiographic signal data from awearable sensor

Description of the Related Art

Abnormal heart rhythms, or arrhythmias, may cause various types ofsymptoms, such as loss of-consciousness, palpitations, dizziness, oreven death. An arrhythmia that causes such symptoms is often anindicator of significant underlying heart disease. It is important toidentify when such symptoms are due to an abnormal heart rhythm, sincetreatment with various procedures, such as pacemaker implantation orpercutaneous catheter ablation, can successfully ameliorate theseproblems and prevent significant symptoms and death. For example,monitors such as Holter monitors and similar devices are currently inuse to monitor heart rhythms.

BRIEF SUMMARY OF EMBODIMENTS

Embodiments described herein are directed to a physiological monitoringdevice that may be worn continuously and comfortably by a human oranimal subject for at least one week or more and more typically two tothree weeks or more. In one embodiment, the device is specificallydesigned to sense and record cardiac rhythm (for example,electrocardiogram, ECG) data, although in various alternativeembodiments one or more additional physiological parameters may besensed and recorded. Such physiological monitoring devices may include anumber of features to facilitate and/or enhance the patient experienceand to make diagnosis of cardiac arrhythmias more accurate and timely.

In some embodiments, an electronic device for monitoring physiologicalsignals in a mammal comprises: at least two flexible wings extendinglaterally from a rigid housing, wherein the flexible wings comprise afirst set of materials which enable the wings to conform to a surface ofthe mammal and the rigid housing comprises a second set of materials; aprinted circuit board assembly housed within the rigid housing, whereinthe rigid housing is configured to prevent deformation of the printedcircuit board in response to movement of the mammal; at least twoelectrodes embedded within the flexible wings, the electrodes configuredto provide conformal contact with the surface of the mammal and todetect the physiological signals of the mammal; at least two electrodetraces embedded within the wings and mechanically decoupled from therigid housing, the electrode traces configured to provide conformalcontact with the surface of the mammal and transmit electrical signalsfrom the electrodes to the printed circuit board assembly; and, at leastone hinge portion connecting the wings to the rigid housing, the hingeportions configured to flex freely at the area where it is joined to therigid housing.

In certain embodiments, each wing may comprise an adhesive. Inembodiments, the electrodes can be in the same plane as the adhesive. Incertain embodiments, each wing comprises at least one rim, wherein therim is thinner than an adjacent portion of each wing. The rigid housingmay further comprise dimples configured to allow for airflow between therigid housing and the surface of the mammal. In certain embodiments, therim is configured to prevent the release of a portion of the wing fromthe surface of the mammal. In some embodiments, an electronic device formonitoring physiological systems may comprise a measuring instrumentconfigured to detect motion signals in at least one axis. This measuringinstrument may be an accelerometer that can be configured to detectmotion signals in three axes.

In embodiments, the motion signals can be collected in time with thephysiological signals. In certain embodiments, a motion artifact isidentified when the physiological signals and the motion signals match.Further embodiments may call for an event trigger coupled to the printedcircuit board assembly. In some embodiments, the event trigger input issupported by the rigid housing so as to prevent mechanical stress on theprinted circuit board when the trigger is activated which, in turn, canreduce a source of artifact in the recorded signal. The event triggermay be concave and larger than a human finger such that the eventtrigger is easily located. In certain embodiments, the electrode tracesare configured to minimize signal distortion during movement of themammal. In particular embodiments, gaskets may be used as a means forsealable attachment to the rigid housing.

In certain embodiments, a method for monitoring physiological signals ina mammal may comprise: attaching an electronic device to the mammal,wherein the device comprises: at least two electrodes configured todetect physiological signals from the mammal, at least one measuringinstrument configured to detect secondary signals, and at least twoelectrode traces connected to the electrodes and a rigid housing; and,comparing the physiological signals to the secondary signals to identifyan artifact.

In certain embodiments, identification of artifacts comprises acomparison between the frequency spectrum of the physiological signalsand the frequency spectrum of the secondary signals. In embodiments, thesecondary signals comprise motion signals that may be used to derive theactivity and position of the mammal. In certain embodiments, thesecondary signals are collected in three axes. In some embodiments, atertiary signal may also be collected. In certain embodiments, thesecondary signals comprise information about the connection between theelectronic device and the mammal. In some embodiments, the secondarysignals may be used to detect when the mammal is sleeping.

In some embodiments, a method of removing and replacing portions of amodular physiological monitoring device may comprise: applying thedevice described above to a mammal for a period of time greater than 7days and collecting physiological data; using the device to detect afirst set of physiological signals; removing the device from the surfaceof the mammal; removing a first component from the device; and,incorporating the first component into a second physiological monitoringdevice, the second physiological monitoring device configured to detecta second set of physiological signals.

In some embodiments, the first component is electrically connected toother device components without the use of a permanent connection. Insome embodiments, the device may further comprise spring connections. Incertain embodiments, the first component may be preserved for a seconduse by a rigid housing to prevent damage. In particular embodiments, thefirst component is secured within a device by a mechanism that iscapable of re-securing a second component once the first component isremoved.

Certain embodiments may concern a system for inferring cardiac rhythminformation from time-series data of heart beat intervals, as obtainedfrom either consumer wearable or medical device products. A furtheraspect concerns improvements to the system to enable cardiac rhythminformation to be inferred in a more robust and/or timely manner throughthe use of additional sources of data. This additional data may includesummary statistics or specific signal features derived from an ECG, useractivity time series data derived from an accelerometer, informationrelated to user state, or information related to the day/time of therecording.

In certain embodiments, a system for selective transmission ofelectrocardiographic signal data from a wearable medical sensor, whereQRS refers to the three fiducial points of an ECG recording at the timeof ventricle depolarization, may comprise:

-   -   a. A wearable medical sensor incorporating a QRS detector that        produces a real-time estimate of each R peak location in the ECG    -   b. Transmission of an R-R interval time series together with an        onset time stamp from the sensor to a smartphone or        internet-connected gateway device, according to a predefined        schedule    -   c. Transmission of the R-R interval time series and the onset        time stamp from the smartphone or internet-connected gateway        device to a server    -   d. Server-side algorithmic inference of the most probable        rhythms and their onset/offset times from the R-R interval time        series data    -   e. Filtering the list of inferred heart rhythms according to        specific filter criteria, such that only inferred rhythms        matching the given criteria are retained after filtering    -   f. Transmission of the onset/offset time for each rhythm        remaining after filtering, from the server to the smartphone or        internet-connected gateway device    -   g. Transmission of the onset/offset time for each rhythm        remaining after filtering, from the smartphone or        internet-connected gateway device to the wearable sensor    -   h. Transmission of the section of recorded ECG corresponding to        each onset-offset time pair from the sensor to the smartphone or        internet-connected gateway device    -   i. Transmission of the section of recorded ECG corresponding to        each onset-offset time pair from the smartphone or        internet-connected gateway device to the server

The rhythm filter criteria may be specified by a physician or othermedical professional prior to the use of the wearable sensor by apatient. In other embodiments, the rhythm filter criteria are dynamicand can be updated during the use of the system according to predefinedrules. In some embodiments, these predefined rules may describe anadjustment to the filter criteria based on previous findings during useof the system. In some embodiments, the onset and offset time for eachinferred rhythm may be adjusted such that the resulting duration foreach rhythm is less than a given maximum permissible duration. Computedconfidence measures may be an input to the rhythm filter criteria. Insome embodiments, the system comprises inferring cardiac rhythminformation from R-R interval time series data. In certain embodiments,the cardiac rhythm inference system is implemented as a cloud serviceaccessible via an API.

In certain embodiments, the cardiac rhythm inference system is providedthrough a software library that can be incorporated into a standaloneapplication. The R-R interval values may be are estimated from aphotoplethysmography signal.

In certain embodiments of a method for inferring cardiac rhythminformation, the cardiac rhythm inference system computes a confidencescore for each type of cardiac rhythm, the method comprising:

-   -   a. Computing the frequency and duration of each cardiac rhythm        type inferred from the collection of R-R interval time series        data for the given user    -   b. Estimating a confidence statistic for each rhythm type based        on the inferred frequency and duration of the rhythm across the        collection of R-R interval time series for the given user    -   c. Evaluating if the confidence statistic for each inferred        rhythm exceeds a pre-determined threshold value    -   d. Providing rhythm information back to the calling software        only for those inferred rhythms for which the confidence        statistic exceeds the threshold value

In certain embodiments, the cardiac rhythm inference system acceptsadditional sources of data, comprising one or more of:

-   -   e. User activity time series data measured by an accelerometer    -   f. Information on the specific day and time of each R-R interval        time series recording    -   g. Information on user age, gender, clinical indication for        monitoring, pre-existing medical conditions, medication        information, and medical history    -   h. ECG signal features and summary statistics, such as the mean,        median, standard deviation or sum of the ECG signal sample        values within a given time period    -   i. A confidence rating provided by the measurement device to        indicate the quality of heart beat estimation, for example, for        each beat or for sequential time periods.    -   j. Intra-beat interval measurements

In embodiments, a system for monitoring cardiac signal data, comprises:

-   -   a wearable medical sensor, the wearable medical sensor        configured to detect cardiac signals from a mammal and estimate        the R-peak location within the cardiac signal;    -   wherein the wearable medical sensor is configured to transmit an        R-R interval time series and a time stamp to an intermediary        device, the intermediary device configured to further transmit        the R-R interval time series and time stamp to a server;    -   wherein the server is configured to infer the most probable        rhythms and their onset/offset times from the R-R interval time        series and time stamp, the server configured to filter the most        probable rhythms according to a first criteria into a filtered        data set;    -   wherein the server is configured to transmit the filtered data        set back to the wearable sensor via the intermediary device; and    -   wherein the sensor transmits the full resolution cardiac signal        to the server for a time period surrounding each of the filtered        events.

In certain embodiments, a system for monitoring cardiac signal datacomprises:

-   -   a server configured to communicate with a wearable sensor, the        wearable sensor configured to detect cardiac signals from a        mammal and estimate the R peak location within the cardiac        signal;    -   wherein the wearable sensor is configured to transmit an R-R        interval time series and a time stamp to the server;    -   wherein the server is configured to infer the most probable        rhythms and their onset/offset times from the R-R interval time        series and time stamp, the server configured to filter the most        probable rhythms according to a first criteria into a filtered        data set; and    -   wherein the server is configured to transmit a summary of the        filtered data.

In particular embodiments, a server for monitoring cardiac signal data,comprises:

-   -   a portal configured to communicate with a wearable sensor, the        wearable sensor configured to detect cardiac signals from a        mammal and estimate the R peak location within the cardiac        signal, wherein the wearable sensor is configured to transmit an        R-R interval time series and a time stamp to an intermediary        device, the intermediary device configured to further transmit        the R-R interval time series and time stamp to a server;    -   a processor configured to infer the most probable rhythms and        their onset/offset times from the R-R interval time series and        time stamp, the processor configured to filter the most probable        rhythms according to a first criteria into a filtered data set;        and    -   wherein the server is configured to transmit a summary of the        filtered data set.

In embodiments, a non-transitory storage medium havingcomputer-executable instructions stored thereon, the computer-executableinstructions readable by a computing system comprising one or morecomputing devices, wherein the computer-executable instructions areexecutable on the computing system in order to cause the computingsystem to perform operations comprises: receiving, by a computing systemthrough a communication link, physiological sensor data generated by apatient monitoring device, the physiological sensor data associated witha first patient; analyzing, by the computing system, the physiologicalsensor data to determine whether one or more points in the physiologicaldata that are likely indicative of one or more predetermined set ofconditions; and after determining that at least one of the one or morepoints in the physiological data is likely indicative of at least one ofthe one or more predetermined set of conditions, generating, by thecomputing system, an electronic data package for transmission to thepatient monitoring device, the electronic data package includinglocation data regarding the at least one of the one or more points inthe physiological sensor data that are likely indicative of the at leastone of the one or more predetermined set of conditions.

In certain embodiments, the physiological sensor data may comprise asampling of interval data measured from the recorded signal data, thesampling of interval data of a data size less than the recorded signaldata.

In particular embodiments, a system for monitoring physiological signalsin a mammal may comprise: a wearable adhesive monitor configured todetect and record cardiac rhythm data from a mammal, the wearableadhesive monitor configured to extract a feature from the cardiac rhythmdata; and wherein the wearable adhesive monitor is configured totransmit the feature to a processing device, the processing deviceconfigured to analyze the feature, identify locations of interest, andtransmit the locations of interest back to the wearable adhesivemonitor.

In certain embodiments, a system for assessing physiological sensor datafrom a patient monitoring device comprises: a computer processor andnon-transitory computer-readable media combined with the computerprocessor configured to provide a program that includes a set ofinstructions stored on a first server, the set of instructions beingexecutable by the computer processor, and further configured to executea sensor data inference module of the program; the sensor data inferencemodule of the program storing instructions to: receive physiologicalsensor data generated by a patient monitoring device, the physiologicalsensor data associated with a first patient; analyze the physiologicalsensor data to determine whether one or more points in the physiologicaldata that are likely indicative of one or more predetermined set ofconditions; and after determining that at least one of the one or morepoints in the physiological data is likely indicative of at least one ofthe one or more predetermined set of conditions, generating anelectronic data package for transmission to the patient monitoringdevice, the electronic data package including location data regardingthe at least one of the one or more points in the physiological sensordata that are likely indicative of the at least one of the one or morepredetermined set of conditions.

In certain embodiments, a computerized method may comprise: accessingcomputer-executable instructions from at least one computer-readablestorage medium; and executing the computer-executable instructions,thereby causing computer hardware comprising at least one computerprocessor to perform operations comprising: receiving, by a servercomputer through a communication link, physiological sensor datagenerated by a patient monitoring device, the physiological sensor dataassociated with a first patient; analyzing, by the server computer, thephysiological sensor data to determine whether one or more points in thephysiological data that are likely indicative of one or morepredetermined set of conditions; and after determining that at least oneof the one or more points in the physiological data is likely indicativeof at least one of the one or more predetermined set of conditions,generating, by the server computer, an electronic data package fortransmission to the patient monitoring device, the electronic datapackage including location data regarding the at least one of the one ormore points in the physiological sensor data that are likely indicativeof the at least one of the one or more predetermined set of conditions.

These and other aspects and embodiments of the invention are describedin greater detail below, with reference to the drawing figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B are perspective and exploded profile views,respectively, of a physiological monitoring device, according to oneembodiment.

FIGS. 2A and 2B are top perspective and bottom perspective views,respectively, of a printed circuit board assembly of the physiologicalmonitoring device, according to one embodiment.

FIGS. 3A, 3B, 3C, 3D, and 3E are perspective and exploded views of aflexible body and gasket of the physiological monitoring device,according to one embodiment.

FIG. 4 is an exploded view of a rigid housing of the physiologicalmonitoring device; according to one embodiment.

FIGS. 5A and 5B provide a perspective view of a battery holder of thephysiological monitoring device, according to one embodiment.

FIGS. 6A and 6B are cross sectional views of the physiologicalmonitoring device;, according to one embodiment.

FIG. 7 is an exploded view of the physiological monitoring deviceincluding a number of optional items, according to one embodiment.

FIGS. 8A and 8B are perspective views of two people wearing thephysiological monitoring device, illustrating how the device bends toconform to body movement and position, according to one embodiment.

FIGS. 9A, 9B, 9C, 9D, 9E, and 9F illustrate various steps for applyingthe physiological monitor to a patient's body, according to oneembodiment.

FIG. 10 illustrates a schematic diagram of an embodiment of a cardiacrhythm inference service.

FIG. 11 is a schematic diagram of an embodiment of a system forextracting and transmitting data features from a physiological monitor.

FIG. 12 is a schematic diagram of an embodiment of a system forextracting and transmitting data features from a physiological monitorusing a transmitting device.

FIG. 13 is a schematic diagram of an embodiment of a physiologicalmonitoring system utilizing additional data channels.

FIG. 14 is a schematic diagram of an embodiment of a physiologicalmonitoring system incorporating data filters.

FIG. 15 is a schematic diagram of an embodiment of a wearable devicesystem.

FIG. 16 is a schematic diagram of an embodiment of a symptomatictransmission system.

FIG. 17 is a schematic diagram of an embodiment of an asymptomatictransmission system.

FIG. 18 is a schematic diagram of an embodiment of a computer networksystem.

FIG. 19 is a schematic diagram of an embodiment of a programming anddistribution module.

DETAILED DESCRIPTION OF EMBODIMENTS

The following description is directed to a number of variousembodiments. The described embodiments, however, may be implementedand/or varied in many different ways. For example, the describedembodiments may be implemented in any suitable device, apparatus, orsystem to monitor any of a number of physiological parameters. Forexample, the following discussion focuses primarily on long-term,patch-based cardiac rhythm monitoring devices. In one alternativeembodiment, a physiological monitoring device may be used, for example,for pulse oximetry and diagnosis of obstructive sleep apnea. The methodof using a physiological monitoring device may also vary. In some cases,a device may be worn for one week or less, while in other cases, adevice may be worn for at least seven days and/or for more than sevendays, for example between fourteen days and twenty-one days or evenlonger. Many other alternative embodiments and applications of thedescribed technology are possible. Thus, the following description isprovided for exemplary purposes only. Throughout the specification,reference may be made to the term “conformal.” It will be understood byone of skill in the art that the term “conformal” as used herein refersto a relationship between surfaces or structures where a first surfaceor structure adapts to the contours of a second surface or structure.

Since abnormal heart rhythms or arrhythmias can often be due to other,less serious causes, a key challenge is to determine when any of thesesymptoms are due to an arrhythmia. Oftentimes, arrhythmias occurinfrequently and/or episodically, making rapid and reliable diagnosisdifficult. As mentioned above, currently, cardiac rhythm monitoring isprimarily accomplished through the use of devices, such as Holtermonitors, that use short-duration (less than 1 day) electrodes affixedto the chest. Wires connect the electrodes to a recording device,usually worn on a belt. The electrodes need daily changing and the wiresare cumbersome. The devices also have limited memory and recording time.Wearing the device interferes with patient movement and often precludesperforming certain activities while being monitored, such as bathing.Further, Holter monitors are capital equipment with limitedavailability, a situation that often leads to supply constraints andcorresponding testing delays. These limitations severely hinder thediagnostic usefulness of the device, the compliance of patients usingthe device, and the likelihood of capturing all important information.Lack of compliance and the shortcomings of the devices often lead to theneed for additional devices, follow-on monitoring, or other tests tomake a correct diagnosis.

Current methods to correlate symptoms with the occurrence ofarrhythmias, including the use of cardiac rhythm monitoring devices,such as Holter monitors and cardiac event recorders, are often notsufficient to allow an accurate diagnosis to be made. In fact, Holtermonitors have been shown to not lead to a diagnosis up to 90% of thetime (“Assessment of the Diagnostic Value of 24-Hour AmbulatoryElectrocardiographic Monitoring”, by DE Ward et al. Biotelemetry PatientMonitoring, vol. 7, published in 1980).

Additionally, the medical treatment process to actually obtain a cardiacrhythm monitoring device and initiate monitoring is typically verycomplicated. There are usually numerous steps involved in ordering,tracking, monitoring, retrieving, and analyzing the data from such amonitoring device. In most cases, cardiac monitoring devices used todayare ordered by a cardiologist or a cardiac electrophysiologist (EP),rather than the patient's primary care physician (PCP). This is ofsignificance since the PCP is often the first physician to see thepatient and determine that the patient's symptoms could be due to anarrhythmia. After the patient sees the PCP, the PCP will make anappointment for the patient to see a cardiologist or an EP. Thisappointment is usually several weeks from the initial visit with thePCP, which in itself leads to a delay in making a potential diagnosis aswell as increases the likelihood that an arrhythmia episode will occurand go undiagnosed. When the patient finally sees the cardiologist orEP, a cardiac rhythm monitoring device will usually be ordered. Themonitoring period can last 24 to 48 hours (Holter monitor) or up to amonth (cardiac event monitor or mobile telemetry device). Once themonitoring has been completed, the patient typically must return thedevice to the clinic, which itself can be an inconvenience. After thedata has been processed by the monitoring company or by a technicianon-site at a hospital or office, a report will finally be sent to thecardiologist or EP for analysis. This complex process results in fewerpatients receiving cardiac rhythm monitoring than would ideally receiveit.

To address some of these issues with cardiac monitoring, the assignee ofthe present application developed various embodiments of a small,long-term, wearable, physiological monitoring device. One embodiment ofthe device is the Zio® Patch. Various embodiments are also described,for example, in U.S. Pat. Nos. 8,150,502, 8,160,682 8,244,335,8,560,046, and 8,538,503, the full disclosures of which are herebyincorporated herein by reference. Generally, the physiologicalpatch-based monitors described in the above references fit comfortablyon a patient's chest and are designed to be worn for at least one weekand typically two to three weeks. The monitors detect and record cardiacrhythm signal data continuously while the device is worn, and thiscardiac rhythm data is then available for processing and analysis.

These smaller, long-term, patch-based physiological monitoring devicesprovide many advantages over prior art devices. At the same time,further improvements are desired. One of the most meaningful areas forimprovement is to offer more timely notice of critical arrhythmias tomanaging clinicians. The hallmark of these initial embodiments wasthat—for reasons of performance, compliance and cost—the device onlyrecorded information during the extended wear period, with analysis andreporting occurring after the recording completed. Thus, a desirableimprovement would be to add the capability of either real-time or timelyanalysis of the collected rhythm information. While diagnostic monitorswith such timely reporting capabilities currently exist, they requireone or more electrical components of the system to be either regularlyrecharged or replaced. These actions are associated with reduced patientcompliance and, in turn, reduced diagnostic yield. As such, a key areaof improvement is to develop a physiologic monitor that can combinelong-term recording with timely reporting without requiring batteryrecharging or replacement.

Patient compliance and device adhesion performance are two factors thatgovern the duration of the ECG record and consequently the diagnosticyield. Compliance can be increased by improving the patient's wearexperience, which is affected by wear comfort, device appearance, andthe extent to which the device impedes the normal activities of dailyliving. Given that longer ECG records provide greater diagnostic yieldand hence value, improvements to device adhesion and patient complianceare desirable.

Signal quality is important throughout the duration of wear, but may bemore important where the patient marks the record, indicating an area ofsymptomatic clinical significance. Marking the record is most easilyenabled through a trigger located on the external surface of the device.However, since the trigger may be part of a skin-contacting platformwith integrated electrodes, the patient can introduce significant motionartifacts when feeling for the trigger. A desirable device improvementwould be a symptom trigger that can be activated with minimal additionof motion artifact.

Further, it is desirable for the device to be simple and cost effectiveto manufacture, enabling scalability at manufacturing as well as higherquality due to repeatability in process. Simplicity of manufacture canalso lead to ease of disassembly, which enables the efficient recoveryof the printed circuit board for quality-controlled reuse in anotherdevice. Efficient reuse of this expensive component can be important fordecreasing the cost of the diagnostic monitor.

There remain clinical scenarios where still longer-duration andlower-cost solutions may be a valuable addition to a portfolio ofcardiac ambulatory monitoring options. Inspiration for a potentialsolution to these needs can be found in the continuous heart ratesensing functionality that is increasingly being incorporated in avariety of consumer health and fitness products, including smart watchesand wearable fitness bands. Although continuous heart rate data can beused to provide the user with information about their general fitnesslevels, it is more both more challenging and valuable to use this datato provide meaningful information related to their health and wellness.For example, the ability to detect potential arrhythmias from continuousheart rate data would enable consumer devices incorporating heart ratesensing functionality to serve as potential screening tools for theearly detection of cardiac abnormalities. Such an approach could beclinically valuable in providing a long-term, cost-effective screeningmethod for at-risk populations, for example, heart failure patients atrisk for Atrial Fibrillation. Alternatively, this monitoring approachcould be helpful in the long-term titration of therapeutic drug dosagesto ensure efficaciousness while reducing side effects, for example, inthe management of Paroxysmal Atrial Fibrillation. Beyond cardiacarrhythmia detection, the appropriate analysis of heart rate informationcould also yield insight into sleep and stress applications.

Long-term ambulatory monitoring with a physiologic device, such as anadhesive patch, has a number of clinical applications, particularly whentimely information about the occurrence and duration of observedarrhythmias can be provided during the monitoring period. In terms ofprevalence, particularly as driven by an aging population, efficientlydetecting Atrial Fibrillation (AF) remains the most significantmonitoring need. This need is not just evident for patients presentingwith symptoms, but also—given the increased risk of stroke associatedwith this arrhythmia—for broader, population-based monitoring ofasymptomatic AF in individuals at risk due to one or more factors ofadvanced age, the presence of chronic illnesses like Heart Disease, oreven the occurrence of surgical procedures. For the latter group, bothperioperative and post-procedure monitoring can be clinically valuable,and not just for procedures targeted at arrhythmia prevention (forexample, the MAZE ablation procedure, or hybrid endo and epicardialprocedures, both for treatment of AF), but also for general surgeriesinvolving anesthesia. For some applications, the goal of ambulatorymonitoring for Atrial Fibrillation will sometimes be focused on thesimple binary question of yes or no—did AF occur in a given time period.For example, monitoring a patient following an ablation procedure willtypically seek to confirm success, typically defined as the completelack of AF occurrence. Likewise, monitoring a patient post-stroke willbe primarily concerned with evaluating the presence of AtrialFibrillation.

However, even in those scenarios, if AF occurs, it may be clinicallymeaningful to evaluate additional aspects to better characterize theoccurrence, such as daily burden (% of time in AF each day), andduration of episodes (expressed, for example, as a histogram of episodeduration, or as the percentage of episodes that extend beyond aspecified limit, say six minutes), both either in absolute terms or incomparison to prior benchmarks (for example, from a baseline,pre-procedure monitoring result). Indeed, measuring daily AF burden,evaluating AF episode duration, and reviewing AF occurrence during sleepand waking periods, and evaluating the presence of AF in response to thedegree of a patient's physical movement can be important in a variety ofclinical scenarios, including evaluating the effectiveness of drug-basedtreatment for this arrhythmia.

Making this information available in a timely manner during themonitoring period could allow the managing physician to iterativelytitrate treatment, for example, by adjusting the dosage and frequency ofa novel oral anticoagulant drug (NOAC) until management was optimized. Afurther example of this management paradigm is for the patient to benotified of asymptomatic AF—either directly by the device throughaudible or vibration-based alert, through notification from anapplication connected to the device, or via phone, email or text-messagecommunication from the managing clinician—for the timely application ofa “pill in the pocket” for AF management.

The theme of timely management and/or intervention is certainly evidentin situations where clinically significant arrhythmias are observed, forexample, asymptomatic second-degree and complete Heart Block, extendedpauses, high-rate supraventricular tachycardias, prolonged ventriculartachycaridas, and ventricular fibrillation. For example, the clinicalscenario where an extended pause or complete heart block causes Syncopeis a particularly significant case where the availability of a timelyand dependable monitoring method could reduce or even eliminate the needfor in-hospital monitoring of at-risk patients. The theme can alsoextend to more subtle changes in morphology, for example, QTprolongation in response to medications, which has been shown to havesignificant cardiac safety implications. Timely awareness of suchprolongation could lead, for example, to early termination of clinicalstudies evaluating drug safety and effectiveness or, alternatively, toadjusting the dosage or frequency as a means to eliminate observedprolongation.

Physiological Monitoring Devices

Referring to FIGS. 1A and 1B, perspective and exploded profile views ofone embodiment of a physiological monitoring device 100 are provided. Asseen in FIG. 1A, physiological monitoring device 100 may include aflexible body 110 coupled with a watertight, rigid housing 115. Flexiblebody 110 (which may be referred to as “flexible substrate” or “flexibleconstruct”) typically includes two wings 130, 131, which extendlaterally from rigid housing 115, and two flexible electrode traces 311,312, each of which is embedded in one of wings 130, 131. Each electrodetrace 311, 312 is coupled, on the bottom surface of flexible body 110,with a flexible electrode (not visible in FIG. 1A). The electrodes areconfigured to sense heart rhythm signals from a patient to whichmonitoring device 100 is attached. Electrode traces 311, 312 thentransmit those signals to electronics (not visible in FIG. 1A) housed inrigid housing 115. Rigid housing 115 also typically contains a powersource, such as one or more batteries.

The combination of a highly flexible body 110, including flexibleelectrodes and electrode traces 311, 312, with a very rigid housing 115may provide a number of advantages. A key advantage is high fidelitysignal capture. The highly conformal and flexible wings 130, 131,electrodes and traces 311, 312 limit the transmission of external energyto the electrode-skin interface. If motion is imparted to the rigidhousing 115, for example, the system of conformal adhesion to the skinlimits the extent to which that motion affects the monitored signal.Flexible electrode traces 311, 312 generally may help provide conformalcontact with the subject's skin and may help prevent electrodes 350(electrodes 350 are not visible in FIG. 1, but are visible in FIG. 6Adescribed below) from peeling or lifting off of the skin, therebyproviding strong motion artifact rejection and better signal quality byminimizing transfer of stress to electrodes 350. Furthermore, flexiblebody 110 includes a configuration and various features that facilitatecomfortable wearing of device 100 by a patient for fourteen (14) days ormore without removal. Rigid housing 115, which typically does not adhereto the patient in the embodiments described herein, includes featuresthat lend to the comfort of device 100. Hinge portions 132 arerelatively thin, even more flexible portions of flexible body 110. Theyallow flexible body 110 to flex freely at the area where it is joined torigid housing 115. This flexibility enhances comfort, since when thepatient moves, housing 115 can freely lift off of the patient's skin.Electrode traces 311, 312 are also very thin and flexible, to allow forpatient movement without signal distortion.

Referring now to FIG. 1B, a partially exploded view of physiologicalmonitoring device 100 illustrates component parts that make up, and thatare contained within, rigid housing 115 in greater detail. In thisembodiment, rigid housing 115 includes an upper housing member 140,which detachably couples with a lower housing member 145. Sandwichedbetween upper housing member 140 and lower housing member 145 are anupper gasket 370, and a lower gasket 360 (not visible on FIG. 1B butjust below upper gasket 370). Gaskets 370, 360 help make rigid housingmember 115 watertight when assembled. A number of components ofmonitoring device 100 may be housed between upper housing member 140 andlower housing member 145. For example, in one embodiment, housing 115may contain a portion of flexible body 110, a printed circuit boardassembly (PCBA) 120, a battery holder 150, and two batteries 160.Printed circuit board assembly 120 is positioned within housing 115 tocontact electrode traces 311, 312 and batteries 160. In variousembodiments, one or more additional components may be contained withinor attached to rigid housing 115. Some of these optional components aredescribed further below, in reference to additional drawing figures.

Battery holder 150, according to various alternative embodiments, mayhold two batteries (as in the illustrated embodiment), one battery, ormore than two batteries. In other alternative embodiments, other powersources may be used. In the embodiment shown, battery holder 150includes multiple retain tabs 153 for holding batteries 160 in holder150. Additionally, battery holder 150 includes multiple feet 152 toestablish correct spacing of batteries 160 from the surface of PCBA 120and ensure proper contact with spring fingers 235 and 236. Springfingers 235 and 236 are used in this embodiment rather than solderingbatteries 160 to PCBA 120. Although soldering may be used in alternativeembodiments, one advantage of spring fingers 235 and 236 is that theyallow batteries 160 to be removed from PCBA 120 and holder 150 withoutdamaging either of those components, thus allowing for multiple reusesof both. Eliminating solder connections also simplifies and speeds upassembly and disassembly of monitoring device 100.

In some embodiments, upper housing member 140 may act as a patient eventtrigger. When a patient is wearing physiological monitoring device 100for cardiac rhythm monitoring, it is typically advantageous for thepatient to be able to register with device 100 (for example, log intothe device's memory) any cardiac events perceived by the patient. If thepatient feels what he/she believes to be an episode of heart arrhythmia,for example, the patient may somehow trigger device 100 and thus providea record of the perceived event. In some embodiments, trigger ofperceived events by the patient may initiate transmission of dataassociated with the triggered event. In some embodiments, trigger ofperceived events may simply mark a continuous record with the locationof the triggered event. In some embodiments, both transmission ofassociated data as well as marking of the continuous record may occur.At some later time, the patient's recorded symptom during the perceivedevent could be compared with the patient's actual heart rhythm, recordedby device 100, and this may help determine whether the patient'sperceived events correlate with actual cardiac events. One problem withpatient event triggers in currently available wearable cardiac rhythmmonitoring devices, however, is that a small trigger may be hard to findand/or activate, especially since the monitoring device is typicallyworn under clothing. Additionally, pressing a trigger button may affectthe electronics and/or the electrodes on the device in such a way thatthe recorded heart rhythm signal at that moment is altered simply by themotion caused to the device by the patient triggering. For example,pressing a trigger may jar one or both of the electrodes in such a waythat the recorded heart rhythm signal at that moment appears like anarrhythmia, even if no actual arrhythmia event occurred. Additionally,there is a chance that the trigger may be inadvertently activated, forinstance while sleeping or laying on the monitoring device.

In the embodiment shown in FIGS. 1A and 1B, however, rigid housing 115is sufficiently rigid, and flexible body 110 is sufficiently flexible,that motion applied to housing 115 by a patient may rarely or ever causean aberrant signal to be sensed by the electrodes. In this embodiment,the central portion of upper housing member 140 is slightly concave and,when pressed by a patient who is wearing device 100, this centralportion depresses slightly to trigger a trigger input on PCBA 120.Because the entire upper surface of rigid housing 115 acts as thepatient event trigger, combined with the fact that it is slightlyconcave, it will generally be quite easy for a patient to find and pushdown the trigger, even under clothing. Additionally, the concave natureof the button allows it to be recessed which protects it frominadvertent activations. Thus, the present embodiment may alleviate someof the problems encountered with patient event triggers on currentlyavailable heart rhythm monitors. These and other aspects of the featuresshown in FIGS. 1A and 1B will be described in further detail below.

Referring now to the embodiments in FIGS. 2A and 2B, printed circuitboard assembly 120 (or PCBA) may include a top surface 220, a bottomsurface 230, a patient trigger input 210 and spring contacts 235, 236,and 237. Printed circuit board assembly 120 may be used to mechanicallysupport and electrically connect electronic components using conductivepathways, tracks or electrode traces 311, 312. Furthermore, because ofthe sensitive nature of PCBA 120 and the requirement to mechanicallyinterface with rigid body 115, it is beneficial to have PCBA 120 besubstantially rigid enough to prevent unwanted deflections which mayintroduce noise or artifact into the ECG signal. This is especiallypossible during patient trigger activations when a force is transmittedthrough rigid body 115 and into PCBA 120. One way to ensure rigidity ofthe PCBA is in some embodiments, to ensure that the thickness of thePCBA is relatively above a certain value. For example, a thickness of atleast about 0.08 cm is desirable and, more preferably, a thickness of atleast about 0.17 cm is desirable. In this application, PCBA 120 may alsobe referred to as, or substituted with, a printed circuit board (PCB),printed wiring board (PWB), etched wiring board, or printed circuitassembly (PCA). In some embodiments, a wire wrap or point-to-pointconstruction may be used in addition to, or in place of, PCBA 120. PCBA120 may include analog circuits and digital circuits.

Patient trigger input 210 may be configured to relay a signal from apatient trigger, such as upper housing member 140 described above, toPCBA 120. For example, patient trigger input 210 may be a PCB switch orbutton that is responsive to pressure from the patient trigger (forexample, the upper surface of upper housing portion 140). In variousembodiments, patient trigger input 210 may be a surface mounted switch,a tactile switch, an LED illuminated tactile switch, or the like. Insome embodiments, patient trigger input 210 may also activate anindicator, such as an LED. Certain embodiments may involve a remotelylocated trigger such as on a separate device or as a smart phone app.

One important challenge in collecting heart rhythm signals from a humanor animal subject with a small, two-electrode physiological monitoringdevice such as device 100 described herein, is that having only twoelectrodes can sometimes provide a limited perspective when trying todiscriminate between artifact and clinically significant signals. Forexample, when a left-handed patient brushes her teeth while wearing asmall, two-electrode physiological monitoring device on her left chest,the tooth brushing may often introduce motion artifact that causes arecorded signal to appear very similar to Ventricular Tachycardia, aserious heart arrhythmia. Adding additional leads (and, hence, vectors)is the traditional approach toward mitigating this concern, but this istypically done by adding extra wires adhered to the patient's chest invarious locations, such as with a Holter monitor. This approach is notconsistent with a small, wearable, long term monitor such asphysiological monitoring device 100.

An alternate approach to the problem described above is to provide oneor more additional data channels to aid signal discrimination. In someembodiments, for example, device 100 may include a data channel fordetecting patch motion. In certain embodiments, an accelerometer orother suitable device may provide patch motion by simply analyzing thechange in magnitude of a single axis measurement, or alternatively ofthe combination of all three axes. The accelerometer may record devicemotion at a sufficient sampling rate to allow algorithmic comparison ofits frequency spectrum with that of the recorded ECG signal. If there isa match between the motion and recorded signal, it is clear that thedevice recording in that time period is not from a clinical (forexample, cardiac) source, and thus that portion of the signal can beconfidently marked as artifact. This technique may be particularlyuseful in the tooth brushing motion example aforementioned, where therapid frequency of motion as well as the high amplitude artifact issimilar to the heart rate and morphology, respectively, of a potentiallylife-threatening arrhythmia like Ventricular Tachycardia. Other suitabledevices described herein this section and elsewhere in the specificationmay also be utilized to provide motion information.

In some embodiments, using the magnitude of all three axes for such ananalysis would smooth out any sudden changes in values due to a shift inposition rather than a change in activity. In other embodiments, theremay be some advantage in using a specific axis of measurement such asalong the longitudinal axis of the body to focus on a specific type ofartifact introduced by upward and downward movements associated withwalking or running. In a similar vein, the use of a gyroscope inconjunction with the accelerometer may provide further resolution as tothe nature of the motion experienced. While whole body movements may besufficiently analyzed with an accelerometer on its own, specific motionof interest such as rotational motion due to arm movement issufficiently complex that an accelerometer alone might not be able todistinguish.

In addition to detecting motion artifact, an accelerometer tuned to thedynamic range of human physical activities may provide activity levelsof the patient during the recording, which can also enhance accuracy ofalgorithmic true arrhythmia detection. Given the single-lead limitationof device 100, arrhythmias that require observation of less prominentwaves (for example P-wave) in addition to rate changes such asSupraventricular Tachycardia pose challenges to both computerizedalgorithms as well as the trained human eye. This particular arrhythmiais also characterized by the sudden nature of its onset, which may bemore confidently discriminated from a non-pathological Sinus Tachycardiaif a sudden surge in the patient's activity level is detected at thesame time as the increase in heart rate. Broadly speaking, the provisionof activity information to clinical professionals may help themdiscriminate between exercise-induced arrhythmia versus not. As withmotion artifact detection, a single-axis accelerometer measurementoptimized to a particular orientation may aid in more specificallydetermining the activity type such as walking or running. Thisadditional information may help explain symptoms more specifically andthereby affect the subsequent course of therapeutic action.

In certain embodiments, an accelerometer with 3 axes may conferadvantages beyond what magnitude of motions can provide. When thesubject is not rapidly moving, 3-dimensional accelerometer readings mayapproximate the tilt of PCBA 120, and therefore body orientationrelative to its original orientation. The original body orientation canbe assumed to be in either an upright or supine position which isrequired for appropriate positioning and application of the device tothe body. This information may aid in ruling out certain cardiacconditions that manifest as beat-to-beat morphology changes, such ascardiac alternans where periodic amplitude changes are observed, oftenin heart failure cases. Similar beat-to-beat morphology changes areobservable in healthy subjects upon shift in body position due to theshift in heart position relative to the electrode vector, for examplefrom an upright to a slouching position. By design, the single-channeldevice 100 does not have an alternate ECG channel to easily rule outpotential pathological shifts in morphology, however, correlation withshifts in body orientation will help explain these normal changes andavoid unnecessary treatment due to false diagnosis.

In other embodiments, the accelerometer may also be used as a sleepindicator, based on body orientation and movement. When presentingclinical events (for example, pauses), it is diagnostically helpful tobe able to present information in a manner that clearly separates eventsthat occurred during sleep from those during waking hours. In fact,certain algorithms such as for ECG-derived respiratory rate only makesense to run when the patient is in a relatively motionless state andtherefore subtle signal modulation introduced by chest movement due tobreathing is observable. Respiratory rate information is useful as onechannel of information necessary to detect sleep apnea in certainpatient populations.

In certain embodiments, the accelerometer may also be used to detectfree-falls, such as fainting. With an accelerometer, device 100 may beable to mark fainting (syncope) and other free-fall events withoutrelying on patient trigger. In some embodiments, such free-fall eventtriggers may initiate transmission of associated data. In order to allowtimely detection of such critical events, yet considering the batteryand memory limitations of a small, wearable device such as device 100,acquisition of accelerometer readings may be done in bursts, where onlyinteresting information such as a potential free fall is written tomemory at a high sampling rate. An expansion of this event-triggerconcept is to use specific tapping motions on device 100 as a patienttrigger instead of or in conjunction with the button previouslydescribed. The use and detection of multiple types of tapping sequencesmay provide better resolution and accuracy into what exactly the patientwas feeling, instead of relying on the patient to manually record theirsymptom and duration in a trigger log after the fact. An example of suchadded resolution is to indicate the severity of the symptom by thenumber of sequential taps.

Alternatively, in other embodiments, optical sensors may be used todistinguish between device motion and patient body motion. Further, inadditional embodiments, the device may not require a button or trigger.In still more embodiments, suitable devices described herein thissection or elsewhere in the specification may also be used.

Another optional data channel that may be added to physiologicalmonitoring device 100 is a channel for detecting flex and/or bend ofdevice 100. In various embodiments, for example, device 100 may includea strain gauge, piezoelectric sensor or optical sensor to detect motionartifact in device 100 itself and thus help to distinguish betweenmotion artifact and cardiac rhythm data. Yet another optional datachannel for device 100 may be a channel for detecting heart rate. Forexample, a pulse oximeter, microphone or stethoscope may provide heartrate information. Redundant heart rate data may facilitatediscrimination of ECG signals from artifact. This is particularly usefulin cases where arrhythmia such as Supraventricular Tachycardia isinterrupted by artifact, and decisions must be made whether the episodewas actually multiple shorter episodes or one sustained episode. Anotherdata channel may be included for detecting ambient electrical noise. Forexample, device 100 may include an antenna for picking upelectromagnetic interference. Detection of electromagnetic interferencemay facilitate discrimination of electrical noise from real ECG signals.Any of the above-described data channels may be stored to support futurenoise discrimination or applied for immediate determination of clinicalvalidity in real-time.

With reference now to the embodiments of FIGS. 3A and 3B, flexible body110 is shown in greater detail. As illustrated in FIG. 3A, flexible body110 may include wings 130, 131, a thin border 133 (or “rim” or “edge”)around at least part of each wing 130, 131, electrode traces 311, 312,and a hinge portion 132 (or “shoulder”) at or near a junction of eachwing 130, 131 with rigid housing 115. Also shown in FIG. 3A is uppergasket 370, which is not considered part of flexible body 110 for thisdescription, but which facilitates attachment of flexible body 110 torigid housing 115.

Hinge portions 132 are relatively thin, even more flexible portions offlexible body 110. They allow flexible body 110 to flex freely at thearea where it is joined to rigid housing 115. This flexibility enhancescomfort, since when the patient moves, housing 115 can freely lift offof the patient's skin. Electrode traces 311, 312 are also very thin andflexible, to allow for patient movement without signal distortion.Borders 133 are portions of flexible body 110 that is thinner thanimmediately adjacent portions and that provide for a smooth transitionfrom flexible body 110 to a patient's skin, thus preventing edge-liftand penetration of dirt or debris below flexible body 110.

As shown in greater detail in FIG. 3B, flexible body 110 may includemultiple layers. As mentioned previously, in some embodiments, uppergasket 370 and lower gasket 360 are not considered part of flexible body110 for the purposes of this description but are shown for completenessof description. This distinction is for ease of description only,however, and should not be interpreted to limit the scope of thedescribed embodiments. Flexible body 110 may include a top substratelayer 300, a bottom substrate layer 330, an adhesive layer 340, andflexible electrodes 350. Top and bottom substrate layers 300, 330 may bemade of any suitable, flexible material, such as one or more flexiblepolymers. Suitable flexible polymers can include, but are not limitedto, polyurethane, polyethylene, polyester, polypropylene, nylon, teflonand carbon impregnated vinyl. The material of substrate layers 300, 330may be selected based on desired characteristics. For example, thematerial of substrate layers 300, 330 may be selected for flexibility,resilience, durability, breathability, moisture transpiration, adhesionand/or the like. In one embodiment, for example, top substrate layer 300may be made of polyurethane, and bottom substrate layer 330 may be madeof polyethylene or alternatively polyester. In other embodiments,substrate layers 300, 330 may be made of the same material. In yetanother embodiment, substrate layer 330 may contain a plurality ofperforations in the area over adhesive layer 340 to provide for evenmore breathability and moisture transpiration. In various embodiments,physiological monitoring device 100 may be worn continuously by apatient for as many as 14-21 days or more, without removal during thetime of wear and with device 100 being worn during showering, exercisingand the like. Thus, the material(s) used and the thickness andconfiguration of substrate layers 300, 330 affect the function ofphysiological monitoring device 100. In some embodiments, the materialof substrate layers 300, 330 acts as an electric static discharge (ESD)barrier to prevent arcing.

Typically, top and bottom substrate layers 300, 330 are attached to oneanother via adhesive placed on one or both layers 300, 330. For example,the adhesive or bonding substance between substrate layers 300, 330 maybe an acrylic-based, rubber-based, or silicone-based adhesive. In otheralternative embodiments, flexible body 110 may include more than twolayers of flexible material.

In addition to the choice of material(s), the dimensions—thickness,length and width—of substrate layers 300, 330 may be selected based ondesired characteristics of flexible body 110. For example, in variousembodiments, the thickness of substrate layers 300, 330 may be selectedto give flexible body 110 an overall thickness of between about 0.1 mmto about 1.0 mm. According to various embodiments, flexible body 110 mayalso have a length of between about 7 cm and 15 cm and a width of about3 cm and about 6 cm. Generally, flexible body 110 will have a lengthsufficient to provide a necessary amount of separation betweenelectrodes 350. For example, in one embodiment a distance from thecenter of one electrode 350 to the center of the other electrode 350should be at least about 6.0 cm and more preferably at least about 8.5cm. This separation distance may vary, depending on the application. Insome embodiments, substrate layers 300, 330 may all have the samethickness. Alternatively, the two substrate layers 300, 330 may havedifferent thicknesses.

As mentioned above, hinge portions 132 allow the rigid body 115 to liftaway from the patient while flexible body 110 remains adhered to theskin. The functionality of hinge portions 132 is critical in allowingthe device to remain adhered to the patient throughout variousactivities that may stretch and compress the skin. Furthermore, hingeportions 132 allow for significantly improved comfort while wearing thedevice. Generally, hinge portions 132 will be sufficiently wide enoughto provide adequate lift of rigid body 115 without creating too large ofa peel force on flexible body 110. For example, in various embodiments,the width of hinge portion 132 should be at least about 0.25 cm and morepreferably at least about 0.75 cm.

Additionally, the shape or footprint of flexible body 110 may beselected based on desired characteristics. As seen in FIG. 3A, wings130, 131 and borders 133 may have rounded edges that give flexible body110 an overall “peanut” shape. However, wings 130, 131 can be formed inany number of different shapes such as rectangles, ovals, loops, orstrips. In the embodiment shown in FIGS. 3A and 3B, the footprint topsubstrate layer 300 is larger than the footprint of bottom substratelayer 330, with the extension of top substrate layer 300 forming borders133. Thus, borders 133 are made of the same polyurethane material thattop layer 300 is made of. Borders 133 are thinner than an adjacentportion of each wing 130, 131, since they includes only top layer 300.The thinner, highly compliant rim 133 will likely enhance adherence ofphysiologic monitoring device 100 to a patient, as it provides atransition from an adjacent, slightly thicker portion of wings 130, 131to the patient's skin and thus helps prevent the edge of device 110 frompeeling up off the skin. Border 133 may also help prevent the collectionof dirt and other debris under flexible body 110, which may help promoteadherence to the skin and also enhance the aesthetics of device 110. Inalternative embodiments, the footprint of substrate layers 300, 330 maybe the same, thus eliminating borders 133.

While the illustrated embodiments of FIGS. 1A-3B include only two wings130, 131, which extend from rigid housing 115 in approximately oppositedirections (for example, at a 180-degree angle relative to each other),other configurations are possible in alternative embodiments. Forexample, in some embodiments, wings 130, 131 may be arranged in anasymmetrical orientation relative to one another and/or one or moreadditional wings may be included. As long as sufficient electrodespacing is provided to permit physiological signal monitoring, and aslong as wings 130, 131 are configured to provide extended attachment tothe skin, any suitable configuration and number of wings 130, 131 andelectrode traces 311, 312 may be used. The embodiments described abovehave proven to be advantageous for adherence, patient comfort andaccuracy of collected heart rhythm data, but in alternative embodimentsit may be possible to implement alternative configurations.

Adhesive layer 340 is an adhesive that is applied to two portions of thebottom surface of bottom substrate layer 330, each portion correspondingto one of wings 130, 131. Adhesive layer 340 thus does not extend alongthe portion of bottom substrate layer 330 upon which rigid housing 115is mounted. Adhesive layer 340 may be made of any suitable adhesive,although certain adhesives have been found to be advantageous forproviding long term adhesion to patient skin with relative comfort andlack of skin irritation. For example, in one embodiment, adhesive layer340 is a hydrocolloid adhesive. In another embodiment, the adhesivelayer 340 is comprised of a hydrocolloid adhesive that containsnaturally-derived or synthetic absorbent materials which take upmoisture from the skin during perspiration.

With reference now to FIG. 3B, each of the two portions of adhesivelayer 340 includes a hole, into which one of electrodes 350 fits.Electrodes 350 are made of flexible material to further provide foroverall conformability of flexible body 110. In one embodiment, forexample, flexible electrodes 350 may be made of a hydrogel 350.Electrodes 350 generally provide conformal, non-irritating contact withthe skin to provide enhanced electrical connection with the skin andreduce motion artifact. In some embodiments, hydrogel electrodes 350 maybe punched into adhesive layer 340, thus forming the holes and fillingthem with hydrogel electrodes 350. In one alternative embodiment,electrodes 350 and adhesive 340 may be replaced with an adhesive layermade of a conductive material, such that the entire adhesive layer onthe underside of each wing 130, 131 acts as an electrode. Such anadhesive layer may include a hybrid adhesive/conductive substance oradhesive substance mixed with conductive elements or particles. Forexample, in one embodiment, such an adhesive layer may be a hybrid of ahydrogel and a hydrocolloid adhesive. Rigid housing 115 of FIG. 1A alsoprotects the electronics and power source contained in housing 120,enhances the ability of a patient to provide an input related to aperceived cardiac event, and allows for simple manufacturing andreusability of at least some of the contents of housing 115. These andother features of physiological monitoring device 100 are described ingreater detail below.

As discussed above, in some embodiments, adhesive layer 340 may cover aportion of the underside of lower substrate layer 330, such that atleast a portion of the bottom side of flexible body 110 does not includeadhesive layer 340. As seen in FIG. 3A, hinges 132 may be formed in theflexible body 110 as portions of each wing 130, 131 on which adhesivelayer 340 is not applied. Hinge portions 132 are generally located at ornear the junction of flexible body 110 with rigid housing 115, and thusprovide for flexing of device 100 to accommodate patient movement. Insome embodiments, hinge portions 132 may have a width that is less thanthat of adjacent portions of wings 130, 131, thus giving device 100 its“peanut” shape mentioned above. As shown in FIG. 8, as a subject moves,device 100 flexes along with patient movement. Device flexion may besevere and is likely to occur many times during long term monitoring.Hinge portions 132 may allow for dynamic conformability to the subject,while the rigidity of rigid housing 115 may allow housing 115 to pop upoff the patient's skin during device flexion, thus preventing peeling ofthe device 100 off of the skin at its edge.

Flexible body 110 further includes two electrode traces 311, 312sandwiched between upper substrate layer 300 and lower substrate layer330. Each electrode trace 311, 312 may include an electrode interfaceportion 310 and an electrocardiogram circuit interface portion 313. Asillustrated in the embodiments of FIGS. 3C and 3D, ECG circuit interfaceportions 313 are in physical contact with spring fingers 237 and provideelectrical communication with PCBA 120 when device 100 or zoomed-indevice portion 101 is assembled. Electrode interface portions 310contact hydrogel electrodes 350. Thus, electrode traces 311, 312transmit cardiac rhythm signals (and/or other physiological data invarious embodiments) from electrodes 350 to PCBA 120.

The material and thickness of electrode traces 311, 312 are importantfor providing a desired combination of flexibility, durability andsignal transmission. For example, in one embodiment, electrode traces311, 312 may include a combination of silver (Ag) and silver chloride(AgCl). The silver and silver chloride may be disposed in layers. Forexample, one embodiment of electrode traces 311, 312 may include a toplayer of silver, a middle layer of carbon impregnated vinyl, and abottom (patient-facing) layer of silver chloride. In another embodiment,both top and bottom layers of electrode traces 311, 312 may be made ofsilver chloride. In one embodiment, the top and bottom layers may beapplied to the middle layer in the form of silver ink and silverchloride ink, respectively. In an alternative embodiment, each electrodetrace may include only two layers, such as a top layer of silver and abottom layer of silver chloride. In various embodiments, the material ofa bottom layer of each electrode trace 311, 312, such as AgCl, may beselected to match the chemistry of the hydrogel electrodes 350 andcreate a half-cell with the body of the subject.

The thickness of the electrode traces 311, 312 may be selected tooptimize any of a number of desirable properties. For example, in someembodiments, at least one of the layers of electrode traces 311, 312 canbe of a sufficient thickness to minimize or slow depletion of thematerial from an anode/cathode effect over time. Additionally, thethickness may be selected for a desired flexibility, durability and/orsignal transmission quality.

As mentioned above, in some embodiments, top gasket 370 and bottomgasket 360 may be attached upper substrate 300 and lower substrate 330of flexible body 110. Gaskets 360, 370 may be made of any suitablematerial, such as urethane, which provides a water tight seal betweenthe upper housing member 140 and lower housing member 145 of rigidhousing 115. In one embodiment, top gasket 370 and/or bottom gasket 360may include an adhesive surface. FIG. 3E depicts yet another embodimentwhere top gasket 370 includes tabs 371 that protrude away from theprofile of top housing 140 while still being adhered to upper substrate300. The tabs 371 cover a portion of electrode traces 311, 312 andprovide a strain relief for the traces at the point of highest stresswhere the flexible body meets the rigid housing.

With reference now to the embodiment of FIG. 4, upper housing member 140and lower housing member 145 of rigid housing 115 are shown in greaterdetail. Upper and lower housing members 140, 145 may be configured, whencoupled together with gaskets 360, 370 in between, to form a watertightenclosure for containing PCBA 120, battery holder 150, batteries 160 andany other components contained within rigid housing 115. Housing members140, 145 may be made of any suitable material to protect internalcomponents, such as water resistant plastic. In one embodiment, upperhousing member 140 may include a rigid sidewall 440, a light pipe 410 totransmit visual information from the LEDs on the PCBA through thehousing member, a slightly flexible top surface 420, and an innertrigger member 430 extending inward from top surface 420. Top surface420 is configured to be depressed by a patient when the patientperceives what he or she believes to be an arrhythmia or other cardiacevent. When depressed, top surface 420 depresses inner trigger member430, which contacts and activates trigger input 210 of PCBA 120.Additionally, as discussed previously, top surface 420 may have aconcave shape (concavity facing the inside of housing 115) toaccommodate the shape of a finger. It is believed that the design ofupper housing member 140 isolates activation of the trigger input 210from electrodes 350, thereby minimizing artifact in the data recording.

With continued reference to FIG. 4, lower housing member 145 may beconfigured to detachably connect with upper housing member 140 in such away that housing members 140, 145 may be easily attached and detachedfor reusability of at least some of the component parts of monitoringdevice 100. In some embodiments, a bottom surface 445 (patient facingsurface) of lower housing member 145 may include multiple dimples 450(or “bumps,” “protrusions” or the like), which will contact thepatient's skin during use. Dimples 450 may allow for air flow betweenbottom surface 445 and the patient's skin, thus preventing a seal fromforming between bottom surface 445 and the skin. It is believed thatdimples 450 improve comfort and help prevent a perception in currentlyavailable devices in which the patient feels as if monitoring device 100is falling off when it housing 115 lifts off the skin and breaks a sealwith the skin. In yet another embodiment the bottom surface 445 of lowerhousing member 450 may include multiple divots (recesses instead ofprotrusions) to prevent a seal from forming.

Referring now to the embodiment of FIG. 5A, battery holder 150 is shownin greater detail. Battery holder 150 may be made of plastic or othersuitable material, is configured to be mounted to PCBA 120 andsubsequently attached to rigid housing 115, and is capable of holdingtwo batteries 160 (FIG. 1B). In alternative embodiments, battery holder150 may be configured to hold one battery or more than two batteries. Aplurality of protrusions 152 provide a stable platform for batteries 160to be positioned a fixed distance above the surface of PCBA 120,avoiding unwanted contact with sensitive electronic components yetproviding for adequate compression of spring contacts 235 (FIG. 5B).Protrusions 153 lock batteries 160 into position and resist the upwardforce on the batteries from spring contacts 235. Battery holder 150 alsopositions batteries appropriately 160 to provide for adequatecompression of spring contacts 236. Use of battery holder 150 inconjunction with spring contacts 235 and 236 allows for batteries 160 tobe electrically connected to PCBA 120 while still having additionalelectronic components between batteries 160 and PCBA 120 and maintain avery compact assembly. Battery holder 150 may include a flexible hook510 which engages a corresponding rigid hook 440 of upper housing member140. Under normal assembly conditions the flexible hook 510 remainssecurely mated with rigid hook 440. For disassembly, flexible hook 510can be pushed and bent using an appropriate tool passed through tophousing 140 causing it to disengage from rigid hook 440 and subsequentlyallow top housing 140 to be removed.

With reference now to the embodiments of FIG. 6A and 6B, physiologicalmonitoring device 100 is shown in side view cross-section. As shown in6A, physiological monitoring device 100 may include flexible body 110coupled with rigid housing 115. Flexible body 110 may include topsubstrate layer 300, bottom substrate layer 330, adhesive layer 340 andelectrodes 350. Electrode traces 311, 312 are also typically part offlexible body 110 and are embedded between top substrate layer 300 andbottom substrate layer 330, but they are not shown in FIG. 6. Flexiblebody 110 forms two wings 130, 131, extending to either side of housing115, and a border 133 surrounding at least part of each wing 130, 131.Rigid housing 115 may include an upper housing member 140 coupled with alower housing member 145 such that it sandwiches a portion of flexiblebody 110 in between and provides a watertight, sealed compartment forPCBA 120. Upper housing member 140 may include inner trigger member 430,and PCBA may include patient trigger member 210. As discussedpreviously, lower housing member 145 may include multiple dimples 450 ordivots to enhance the comfort of the monitoring device 100.

It is desirable that PCBA 120 is sufficiently rigid to prevent bendingand introducing unwanted artifact into the signal. In certainembodiments, an additional mechanism to reduce and prevent unwantedbending of PCBA 120 may be used. This mechanism is shown in FIG. 6B.Support post 460 is integral to lower housing 145 and is positioneddirectly under patient trigger input 210. During patient symptomtriggering, upper housing member 140 is depressed, engaging innertrigger mechanism 430 and transmitting a force through patient triggerinput 210 into PCBA 120. The force is further transmitted through PCBA120 and into support post 460 without creating a bending moment, thusavoiding unwanted artifact.

Referring to FIG. 7, in some embodiments, physiological monitoringdevice 100 may include one or more additional, optional features. Forexample, in one embodiment, monitoring device 100 may include aremovable liner 810, a top label 820, a device identifier 830 and abottom label 840. Liner 810 may be applied over a top surface offlexible member 110 to aid in the application of device 100 to thesubject. As is described in further detail below, liner 810 may helpsupport borders 133 of flexible body 110, as well as wings 130, 131,during removal of one or more adhesive covers (not shown) that coveradhesive surface 340 before use. Liner 810 may be relative rigid and/orfirm, to help support flexible body 110 during removal of adhesivecovers. In various embodiments, for example, liner 810 may be made ofcardboard, thick paper, plastic or the like. Liner 810 typicallyincludes an adhesive on one side for adhering to the top surface ofwings 130, 131 of flexible body 110.

Labels 820, 840 may be any suitable labels and may include producename(s), manufacturer name(s), logo(s), design(s) and/or the like. Theymay be removable or permanently attached upper housing member 140 and/orlower housing member 145, although typically they will be permanentlyattached, to avoid unregulated reuse and/or resale of the device by anunregistered user. Device identifier 830 may be a barcode sticker,computer readable chip, RFID, or the like. Device identifier 830 may bepermanently or removably attached to PCBA 120, flexible body 110 or thelike. In some embodiments, it may be beneficial to have deviceidentifier 830 stay with PCBA 120.

Referring now to the embodiments of FIGS. 8A and 8B, physiologicalmonitoring device 100 generally includes hinge portions 132 at or nearthe juncture of each wing 130, 131 with rigid housing 115. Additionally,each wing 130, 131 is typically adhered to the patient via adhesivelayers 340, while rigid body 115 is not adhered to the patient and isthus free to “float” (for example, move up and down) over the patient'sskin during movement and change of patient position. In other words,when the patient's chest contracts, rigid housing pops up or floats overthe skin, thus minimizing stress on device 100, enhancing comfort, andreducing the tendency of wings 130, 131 to peel off of the skin. Theadvantage provided by the combination of the floating rigid body 115 andthe adhered wings 130, 131 is illustrated in FIGS. 8A and 8B. In FIG.8A, a patient is sleeping, and in FIG. 8B, a patient is playing golf. Inboth examples, monitoring device 100 is squeezed together by thepatient's body, causing rigid housing 115 to float above the skin aswings 130, 131 move closer together. This advantage of a floating,non-attached portion of a physiological monitoring device is describedin further detail in U.S. Pat. No. 8,560,046, which was previouslyincorporated by reference.

Referring now to FIGS. 9A-9F, one embodiment of a method for applyingphysiological monitoring device 100 to the skin of a human subject isdescribed. In this embodiment, before the first step shown in FIG. 9A,the patient's skin may be prepared, typically by shaving a small portionof the skin on the left chest where device 100 will be placed and thenabrading and/or cleaning the shaved portion. As shown in FIG. 9A, oncethe patient's skin is prepared, a first step of applying device 100 mayinclude removing one or both of two adhesive covers 600 from adhesivelayers 340 on the bottom surface of device 100, thus exposing adhesivelayers 340. As illustrated in FIG. 9B, the next step may be to applydevice 100 to the skin, such that adhesive layer 340 adheres to the skinin a desired location. In some embodiments, one adhesive cover 600 maybe removed, the uncovered adhesive layer 340 may be applied to the skin,and then the second adhesive cover 600 may be removed, and the secondadhesive layer 340 may be applied to the skin. Alternatively, bothadhesive covers 600 may be removed before applying device 100 to theskin. While adhesive covers 600 are being removed, liner 810 acts as asupport for flexible body 110, provides the physician or other user withsomething to hold onto, and prevents flexible body 110 and borders 133of flexible body 110 from folding in on themselves, forming wrinkles,and so forth. As described above, liner 810 may be made of a relativelystiff, firm material to provide support for flexible body 110 duringapplication of device 100 to the skin. Referring to FIG. 9C, afterdevice 100 has been applied to the skin, pressure may be applied toflexible body 110 to press it down onto the chest to help ensureadherence of device 100 to the skin.

In a next step, referring to FIG. 9D, liner 810 is removed from (forexample, peeled off of) the top surface of flexible body 110. As shownin FIG. 9E, once liner 810 is removed, pressure may again be applied toflexible body 110 to help ensure it is adhered to the skin. Finally, asshown in FIG. 9F, upper housing member 140 may be pressed to turn onphysiological monitoring device 140. This described method is only oneembodiment. In alternative embodiments, one or more steps may be skippedand/or one or more additional steps may be added.

In certain embodiments, when a desired monitoring period has ended, suchas about 14 to 21 days in some cases, a patient (or physician, nurse orthe like) may remove physiological monitoring device 100 from thepatient's skin, place device 100 in a prepaid mailing pouch, and maildevice 100 to a data processing facility. At this facility, device 100may be partially or completely disassembled, PCBA 120 may be removed,and stored physiological data, such as continuous heart rhythminformation, may be downloaded from device 100. The data may then beanalyzed by any suitable method and then provided to a physician in theform of a report. The physician may then discuss the report with thepatient. PCBA 120 and/or other portions of device 100, such as rigidhousing 115, may be reused in the manufacture of subsequent devices forthe same or other patients. Because device 100 is built up as acombination of several removably coupled parts, various parts may bereused for the same embodiment or different embodiments of device 100.For example, PCBA 120 may be used first in an adult cardiac rhythmmonitor and then may be used a second time to construct a monitor forsleep apnea. The same PCBA 120 may additionally or alternatively be usedwith a differently sized flexible body 110 to construct a pediatriccardiac monitor. Thus, at least some of the component parts of device100 may be interchangeable and reusable.

In further embodiments described in greater detail below, the monitoringdata may be transmitted wirelessly or through other communicationmediums to be analyzed, rather than requiring physical shipment of thedevice for analysis and reporting.

Advantageously, physiological monitoring device 100 may provide longterm adhesion to the skin. The combination of the configuration offlexible and conformal body 110, the watertight, low profileconfiguration of rigid housing 115, and the interface between the twoallows device 100 to compensate for stress caused as the skin of thesubject stretches and bends. As a result, device 100 may be worncontinuously, without removal, on a patient for as many as 14 to 21 daysor more. In some cases, device 100 may be worn for greater or less time,but 14 to 21 days may often be a desirable amount of time for collectingheart rhythm data and/or other physiological signal data from a patient.

In various alternative embodiments, the shape of a particularphysiological monitoring device may vary. The shape, footprint,perimeter or boundary of the device may be circular, an oval,triangular, a compound curve or the like, for example. In someembodiments, the compound curve may include one or more concave curvesand one or more convex curves. The convex shapes may be separated by aconcave portion. The concave portion may be between the convex portionon the rigid housing and the convex portion on the electrodes. In someembodiments, the concave portion may correspond at least partially witha hinge, hinge region or area of reduced thickness between the body anda wing.

While described in the context of a heart monitor, the deviceimprovements described herein are not so limited. The improvementsdescribed in this application may be applied to any of a wide variety ofphysiological data monitoring, recording and/or transmitting devices.The improved adhesion design features may also be applied to devicesuseful in the electronically controlled and/or time released delivery ofpharmacological agents or blood testing, such as glucose monitors orother blood testing devices. As such, the description, characteristicsand functionality of the components described herein may be modified asneeded to include the specific components of a particular applicationsuch as electronics, antenna, power supplies or charging connections,data ports or connections for down loading or off-loading informationfrom the device, adding or offloading fluids from the device, monitoringor sensing elements such as electrodes, probes or sensors or any othercomponent or components needed in the device specific function. Inaddition or alternatively, devices described herein may be used todetect, record, or transmit signals or information related to signalsgenerated by a body including but not limited to one or more of ECG, EEGand/or EMG. In certain embodiments, additional data channels can beinclude to collect additional data, for example, device motion, deviceflex or bed, heart rate and/or ambient electrical or acoustic noise.

The physiological monitors described above and elsewhere in thespecification may further be combined with methods and systems of dataprocessing and transmission that improve the collection of data from themonitor. Further, the methods and systems described below may improvethe performance of the monitors by enabling timely transmission ofclinical information while maintaining the high patient compliance andease-of-use of the monitor described above. For example, the methods andsystems of data processing and transmission described herein thissection of elsewhere in the specification may serve to extend thebattery life of the monitor, improve the accuracy of the monitor, and/orprovide other improvements and advantages as described herein thissection or elsewhere in the specification.

Device Monitoring and Clinical Analysis Platform

The systems and methods described in detail below, in reference to theembodiments of FIGS. 10 to 17, may selectively extract, transmit, andanalyze electrocardiographic signal data and other physiological datafrom a wearable physiological monitor, such as is described above inrelation to FIGS. 1 through 9. The systems and methods described belowcan improve the performance of a wearable physiological monitor thatsimultaneously records and transmits data through multiple means. Forexample, selective transmission of extracted data allows for decreasedpower consumption because the wearable patch is not required to transmitall recorded data. By sending extracted data, much of the analysis maybe performed away from the wearable device without requiring fullon-board rhythm analysis, which can also be highly power consumptive,reducing battery life. Further, remote analysis without the powerconstraints inherent to a wearable device may allow for greatersensitivity and accuracy in analysis of the data. Decreased powerconsumption serves to improve patient compliance because it prolongs thetime period between or even eliminates the need for device replacement,battery changes or battery recharging during the monitoring cycle. Bydecreasing battery consumption, longer monitoring times may be enabledwithout device replacement, for example, at least one week, at least twoweeks, at least three weeks, or more than three weeks.

FIG. 10 depicts a general overview of an embodiment of a system 900 forinferring cardiac rhythm information from an R-R interval time series902, as may be generated by a continuous heart rate monitoring device904. Such systems will be described in much greater detail below inrelation to FIGS. 11 to 17. The R-R interval time series 902 inputted tothe system may include a series of measurements of the timing intervalbetween successive heartbeats. Typically each interval represents thetime period between two successive R peaks as identified from an ECGsignal. R peaks are part of the QRS complex, a combination of threegraphical deflections typically seen on an ECG, representing thedepolarization of the left and right ventricles of a mammal's heart. TheR peak is generally the tallest and most visible upward deflection on anECG, and thus makes for an appropriate reference point. However, infurther embodiments, any characteristic ECG fiducial point (such as theQRS complex onset or offset) may be used in place of the R peak toprovide an estimate of the R-R interval time series. As described abovein relation to FIGS. 1 through 9, the physical characteristics of themonitoring device are constructed in such a way as to improve signalfidelity, therefore the high signal fidelity allows for a high level ofconfidence in accurately extracting R-R peak data.

The R-R interval time series 902 data may be extracted from or receivedfrom a dedicated heart rate monitor such as a heart rate chest strap orheart rate watch, or a wearable health or fitness device 906, 908 thatincorporates heart rate sensing functionality. Alternatively, the R-Rinterval time series 902 may be derived from a wearable patch designedto measure an ECG signal 904 (for instance, by locating the R peaks inthe ECG using a QRS detection algorithm). Furthermore, the R-R intervaltime series 902 may be estimated from an alternative physiologicalsignal such as that obtained from photoplethysmography (PPG). In thisscenario, the peak-to-peak interval time series determined from the PPGsignal may be used as an accurate estimate of the R-R interval timeseries.

In one aspect, a cardiac rhythm inference system 910 is implemented as acloud service or server-based system that exposes an applicationprogramming interface (API) enabling R-R interval time series data orother signal data to be transmitted to the system (for instance, viaHTTP) and the resulting cardiac rhythm information to be returned to thecalling software. The R-R interval time series data 902 or other signaldata may be transmitted to the cloud service directly from theheart-rate monitoring device itself, or indirectly via a smartphone 912,tablet or other internet-enabled communication device 914 that canreceive data from the heart rate monitoring device in either a wirelessor wired manner. In addition, the R-R interval time series data 902 orother signals may be transmitted from a server 916 that stores the datafor a number of users.

In some embodiments, a cardiac rhythm inference system 910 is providedthrough a software library that can be incorporated into a standaloneapplication for installation and use on a smartphone, tablet or personalcomputer. The library may provide identical functionality to that of theinference service, but with R-R interval time series data 902 or othersignal data transmitted directly through a functional call, as opposedto through a web service API.

In certain embodiments, a cardiac rhythm inference system may accept aplurality of R-R interval time series measured from devices of a givenuser 918, in addition to an individual R-R interval time series 902. Inthis scenario, the system computes the frequency and duration of each ofthe cardiac rhythm types inferred from the collection of time seriesdata. These results may then be used to estimate confidence statisticsfor each type of cardiac rhythm based on the frequency and duration ofoccurrence of that rhythm across the various time series. In addition,the rhythm confidence statistics may be updated in a sequential mannerfor each separate call of the inference service. Furthermore, in someembodiments, the cardiac rhythm information inferred by the system maybe provided back to the calling software only in the event that theconfidence score for a given rhythm type exceeds a pre-determinedthreshold value.

In particular embodiments, a cardiac rhythm inference system 910 mayaccept additional sources of data, generally described as alternatesensor channels, in addition to R-R interval time series data, toenhance the accuracy and/or value of the inferred results. Oneadditional source of data includes user activity time series data, suchas that measured by a 3-axis accelerometer concurrently with the R-Rinterval time series measurements. In addition, the system may acceptother relevant metadata that may help to improve the accuracy of therhythm analysis, such as user age, gender, indication for monitoring,pre-existing medical conditions, medication information, medical historyand the like, and also information on the specific day and time rangefor each time series submitted to the system. Furthermore, themeasurement device might also provide some measure of beat detectionconfidence, for example, for each R-Peak or for sequential time periods.This confidence measure would be based on analysis the recorded signalthat, in typical embodiments, would not be recorded due to storage spaceand battery energy requirements. Finally, in the particular case thatthe R-R interval time series data are derived from an ECG signal, thesystem may accept additional signal features computed from the ECG.These features may include a time series of intra-beat intervalmeasurements (such as the QT or PR interval, or QRS duration), or a timeseries of signal statistics such as the mean, median, standard deviationor sum of the ECG signal sample values within a given time period.

The various aspects described above could be used either individually orin combination to provide an application providing insights into anindividual's health, stress, sleep, fitness and/or other qualities.

Some embodiments concern a system for selective transmission ofelectrocardiographic signal data from a wearable medical sensor. Currentwearable sensors, such as the iRhythm ZioPatch™ 904, and furtherdescribed above in relation to FIGS. 1-9, are capable of recording asingle-lead electrocardiogram (ECG) signal for up to two weeks on asingle battery charge. In many situations however, it is desirable forthe sensor to be able to transmit, in real-time or near real-time,specific sections of the recorded ECG signal with clinical relevance toa computer device, such as either a smartphone 912 or aninternet-connected gateway device 914 for subsequent processing andanalysis. In this way, the patient or their physician can be providedwith potentially valuable diagnostic ECG information during the periodthat the patient wears the sensor.

As described above, a significant challenge with this approach is tomanage the battery life of the wearable sensor without requiringreplacement or recharging, both of which reduce user compliance. Eachtransmission of an ECG from the sensor to a smartphone or local gatewaydevice (using, for example, Bluetooth Low Energy) results in asubsequent reduction in the total charge stored in the sensor battery.Some embodiments of the present disclosure, particularly those of FIGS.10 to 17 address this issue through the use of a novel hardware andsoftware combination to enable the selective transmission of clinicallyrelevant sections of ECG from a wearable sensor.

In certain embodiments, the wearable sensor incorporates either asoftware, hardware or hybrid QRS detector that produces a real-timeestimate of each R-peak location in the ECG. The R-peak location data isthen used to compute an R-R interval time series that is subsequentlytransmitted to a smartphone or gateway device according to a predefinedschedule (for example, once per hour). In addition, a time stamp is alsotransmitted which stores the onset time for the R-R interval time seriesrelative to the start of the ECG recording. Since the R-R interval timeseries for a given section of ECG is significantly smaller (in terms ofbytes occupied) than the ECG signal itself, it can be transmitted withconsiderably less impact on battery life.

In some embodiments of a second stage of the system, the R-R intervaltime series together with the onset time stamp is subsequentlytransmitted by the smartphone or gateway device to a server. On theserver, the R-R interval time series is used to infer a list of the mostprobable heart rhythms, together with their onset and offset times,during the period represented by the time series data. The list ofinferred heart rhythms is then filtered according to specific criteria,such that only rhythms matching the given criteria are retained afterfiltering. A measure of confidence may also be used to assist infiltering the events in a manner that might improve the PositivePredictivity of detection.

In certain embodiments of a third stage of the system, for each rhythmin the filtered rhythm set, the server transmits to the smartphone orgateway device the onset and offset time for that specific rhythm. Inthe event that the inferred rhythm duration exceeds a pre-definedmaximum duration, the onset and offset times may be adjusted such thatthe resulting duration is less than the maximum permissible duration.The onset and offset times received by the gateway are then subsequentlytransmitted to the wearable sensor, which in turn transmits the sectionof the recorded ECG signal between the onset and offset times back tothe gateway. This section of ECG is then transmitted to the server whereit can be analyzed and used to provide diagnostic information to thepatient or their physician.

In some embodiments, the system fundamentally allows a device worn forup to about: 14, 21, or 30 days or beyond without battery recharging orreplacement (both activities that reduce patient compliance and,therefore, diagnostic value) to provide timely communication ofasymptomatic arrhythmia events. This development is motivated bytechnology constraints: in order to enable a small, wearable device thatdoes not require battery change or recharging while providing continuousarrhythmia analysis with high accuracy, it is desirable to limit thecomplexity of analysis performed on-board. Similarly, streaming of allof the recorded ECG data to an off-board analysis algorithm may not bepractical without imposing greater power requirements. This motivates amore creative “triage” approach where selected features of the recordedECG signal, including but not limited to R-R intervals, are sent forevery beat, allowing a customized algorithm to locate a number (forexample, 10) of 90-second events to request from the device in fullresolution to support comprehensive analysis, for example, a resolutioncapable of supporting clinical diagnosis.

In other embodiments, the system would provide the ability to detectasymptomatic arrhythmias in a timely manner on a wearable, adhesivelyaffixed device that does not require frequent recharging or replacement.This would be used to enhance the value of some current clinicalofferings, which only provide clinical insight after the recording iscompleted and returned for analysis.

In certain embodiments, the system would allow actionable clinicalinsight to be derived from data collected on low-cost, easy-to-useconsumer wearable devices that are otherwise only focused on fitness andwellness. For example, the technology could be used to create a veryeffective, low-cost screening tool capable of detecting the presence ofAtrial Fibrillation in the at-large population. By using such a tool,not only would patients in need of care be found more easily, but it maybe done earlier and more cost effectively, which lead to betteroutcomes—namely, through reducing stroke risk by identifying AF morequickly.

In particular embodiments, the system may provide the service through adownloadable application that, after receiving customer consent for dataaccess and payment approval, would initiate access and analysis of heartbeat data stored from wearable devices, either stored locally in amobile device or in an online repository. This data pull and analysiswould happen through an Algorithm API, and would result in a clinicalfinding being sent back to the application to be provided to the user.If the data was sufficient to support a “screening oriented” finding,for example, “Likely presence of an irregular rhythm was detected”, theapplication would direct them to a cardiologist where a morediagnostically focused offering, for example, the ZIO® Service, could beprovided to support clinical diagnosis and treatment. In furtherembodiments, as also described elsewhere in the specification, thesystem may trigger an alarm if a particular measurement and/or analysisindicates that an alarm is needed.

Further examples of additional scenarios of clinical value may includecoupling ambulatory arrhythmia monitoring with a blood-alcohol monitorto study the interaction of AF and lifestyle factors. For example,ambulatory arrhythmia monitoring could be coupled with a blood-glucosemonitor to study the impact of Hypoglycemia on arrhythmias.Alternatively, ambulatory arrhythmia monitoring could be coupled with arespiratory rate and/or volume monitor to study the interaction of sleepapnea and breathing disorders. Further, there could be evaluation of thehigh rates of supraventricular ectopic beats as a potential precursorfor AF (for example, 720 SVEs in 24-hour period).

Extraction, Transmission, and Processing Systems

FIG. 11 is a schematic illustration of an embodiment of a system andmethod 1000 for a wearable medical sensor 1002 with transmissioncapabilities, similar to the system and/or method described above inrelation to FIG. 10. In some embodiments, sensor 1002, which may be anytype of sensor or monitor described herein this section or elsewhere inthe specification, continuously senses an ECG or comparable biologicalsignal 1004 and continuously records an ECG or comparable biologicalsignal 1004. In certain embodiments, the sensing and/or recording stepsmay be performed intermittently. The collected signal 1004 may then becontinuously extracted into one or more features 1006, representingexample features A, B, and C. The features are not intended to besamplings of different temporal sections of the signal, instead (as willbe described in greater detail below) the different features maycorrespond to different types or pieces of data such as R-peak locationsor R-peak amplitudes. The features of the ECG or comparable biologicalsignal are extracted to facilitate analysis of the signal 1004 remotely.In certain embodiments, features are extracted on a windowed basis, withthe window size varying for example between 1 hour or multiple hours toa few seconds. In certain embodiments, the window may be at most: about.1 second, about 1 second, about 2 seconds, about 3 seconds, about 5seconds, about 10 seconds, about 30 seconds, about 1 minute, about 5minutes, about 30 minutes, about 1 hour, about 2 hours, about 4 hours,or more than 4 hours. The extraction windows may be separated by variousamounts of time if they are repeated. For example, the extractionwindows may be separated by at least: about 30 seconds, about 1 minute,about 5 minutes, about 30 minutes, about 1 hour, about 3 hours, about 6hours, about 12 hours, about 24 hours, about 48 hours, or more thanthree days. In certain embodiments, the windowing sizes may varydepending on the feature extracted. Feature extraction may be limited toone type or various types of features, and features chosen forextraction may vary depending on the nature of the signal observed.

A wide variety of different types of ECG or comparable biological signalfeatures may be extracted. For example, R-peak locations may beextracted. In certain embodiments, the R-peak locations are extractedvia various methods such as: a Pan-Tompkins algorithm (Pan and Tompkins,1985), providing a real-time QRS complex detection algorithm employing aseries of digital filtering steps and adaptive thresholding, or ananalog R-peak detection circuit comprising an R-peak detector consistingof a bandpass filter, a comparator circuit, and dynamic gain adjustmentto locate R-peaks. The RR-intervals may be calculated from peaklocations and used as the primary feature for rhythm discrimination. Inembodiments, an R-peak overflow flag may be extracted. If more than acertain number of R-peaks were detected during a given time window suchthat not all data can be transmitted, a flag may be raised by thefirmware. Such an extraction may be used to eliminate noisy segmentsfrom analysis, on the basis that extremely short intervals of R-R arenot physiologically possible. With similar motivation, an R-peakunderflow flag may be extracted to indicate an unrealistically longinterval between successive R peaks, provided appropriate considerationsfor asystole are made in this evaluation. In an alternativeimplementation with the same goal, the lack of presence of R peaks in aprolonged interval could be associated with a confidence measure, whichwould describe the likelihood that the interval was clinical orartifact.

Another example of a feature that may be extracted 1006 includes asaturation flag, a firmware or hardware-determined indication that thesignal saturated during a given time window (for example 1 second). Suchan extraction may be used to eliminate noisy segments from analysis. Incertain embodiments, P/T-wave locations may be extracted. This issimilar to R-peak detection, but tuned to lower frequency waves. R-peaklocations may be used to determine the areas of possible wavecomponents. Still another example of a feature that may be extractedincludes the breathing rate. ECG-derived respiration (EDR) may bederived from studying the amplitude modulation of ECG signal amplitude.EDR may be associated with other clinical indicators of arrhythmia. Inembodiments, R-peak amplitude may be extracted, by measuring the ECGsignal amplitude at R-peak locations. This pattern may be studied todiscriminate between true and false peak detection, and/or to detectchanges in beat morphology.

In particular embodiments, the ECG signal amplitude proxy may beextracted. This feature may include: the range of the raw signal dataduring a given time period, the maximum value of the signal during agiven time period, or the minimum value of the signal during a giventime period. This feature may be used as a data point for noisedetection or possible changes in morphology of the ECG (for exampleventricular ectopy). In some embodiments, additional ECG signal samplesmay be extracted. Sampling a few data points at regular intervals orconsecutively from a region in-between selected R-peaks will allow fordetermination of the confidence of rhythm and/or noise classification.Such a selection may be based on R-R interval length. For instance, ifthe interval is longer than 3 seconds, it may be an indicator of apause. Local ECG signal energy may also be extracted, for example bytaking the sum square of signal values within a window centered on apoint of interest, for example an R-peak, thereby providing an integralof ECG sample values in a given time window. This information may beused to characterize the morphology of beats (supraventriculartachycardia (SVT) vs. ventricular tachycardia (VT)).

In certain embodiments, spectral information may be extracted viaextracting statistics from the output from one or more filters, eitherrealized on hardware (during signal acquisition) or firmware. Filtersmay be implemented as a filter bank, such as a short-time Fouriertransform (STFT) or wavelet transform. Such information may be used tocharacterize the morphology of heart beats. Output from simplemachine-learned models may be extracted. For example, the likelihood ofa selected ECG signal segment under a probability model, for exampleGaussian, given raw collected data values or any combination of featuresderived from available channels of data may be extracted. The use of asimple machine-learned model may allow transmission of less data. Inembodiments, the output can directly or indirectly give insight into:the type of underlying rhythm, the presence of ECG features such as aP-wave, the confidence level of R-peak detection, and the presence ofnoise.

Once the feature extraction as described above is completed, variousfeatures 1008 may then be transmitted 1010 to a processing device/server1012. The features 1008 (and alternate sensor channel data and/orfeatures as described below) are transmitted 1010 at regular intervalsto a processor 1012 that is not a physical part of the sensor 1002. Theinterval definition may be pre-set or, configurable with each use, ordynamically configurable. Transmission 1010 of features 1008 may also bebundled and sent when another reason for communication exists, such astransmission of symptomatic data (described in greater detail below inrelation to FIG. 16). In certain embodiments, the processing device 1012may be: a cloud-based server, a physical server at a company location, aphysical server at patient or clinic location, a smartphone, tablet,personal computer, smartwatch, automobile console, audio device and/oran alternate device on or off-site. In particular embodiments, thetransmission 1010 may utilize short-range RF communication protocols,such as: Bluetooth, ZigBee, WiFi (802.11), Wireless USB, ANT or ANT+,Ultrawideband (UWB), and/or custom protocols. The transmission 1010 maybe via infrared communication, such as IrDA and/or inductive couplingcommunication, such as NFC. In certain embodiments, transmission may beaccomplished via cellular data networks and/or wired communicationprotocols, such as: USB, Serial, TDMA, or other suitable custom means.

In some embodiments, the transmitted features 1014 are processed by theremote processor utilizing the data features 1014 to perform analysisvia a rhythm inference system 1016 that analyzes and identifiessegments/locations 1018 likely to include arrhythmia. For example,arrhythmia and ectopy types that may be identified could include: Pause,2^(nd) or 3^(rd) degree AVB, Complete Heart Block, SVT, AF, VT, VF,Bigeminy, Trigeminy, and/or Ectopy. Confidence of determination may beincluded in the identification of rhythms. Further, the rhythm inferencesystem 1016 may also utilize patient demographic data, such as age,gender, or indication to improve accuracy and/or refine confidence indeterminations.

The identified arrhythmia locations 1018 are then transmitted 1020 backto the sensor 1002. The transmission 1020 back to the sensor may beaccomplished by any communication protocols/technology described hereinthis section or elsewhere in the specification, for example viaBluetooth. The sensor then reads the transmitted identified locations1022 and accesses 1024 the areas of memory corresponding to thetransmitted identified locations 1022 of the ECG. In some embodiments,the sensor applies additional analysis of the identified segments tofurther build confidence in the arrhythmia identification. This furtherrhythm confidence determination step 1026 allows for increasing positivepredictivity prior to the power-hungry transmission step. Inembodiments, if the confidence exceeds a defined threshold the datasegment is transmitted. For example, the defined threshold may be apreset value or it may be set per user and monitoring session. Inembodiments, the defined threshold may be changed dynamically dependingon the nature of the rhythm, the history of accurate detection withinthe monitoring period, and/or the confidence of the rhythm inferencesystem. Additional analysis may also be performed. Examples of possibleanalysis techniques include any methods disclosed herein this section orelsewhere in the specification, for example: R-peak amplitude, ECGsignal amplitude proxy, ECG signal samples, local ECG signal energy,spectral information, and/or output from a simple machine-learned model.

If the confidence exceeds a threshold as described above, the sensor1002 may transmit the requested ECG segments 1028 to the processingdevice via any transmission means described herein this section orelsewhere in the specification. The processing device may completefurther analysis on the segments to confirm accuracy of predictedarrhythmia before using data to report to a user and/or physician, asneeded.

FIG. 12 is a schematic illustration of an embodiment of a system andmethod 2000 for a wearable ECG and/or medical sensor 2002 withtransmission capabilities very similar to the system and/or method 1000described above in relation to FIG. 11. The system of FIG. 12 differsfrom the system of FIG. 11 in that it includes secondary transmittingdevices 2004. For example, possible secondary transmitting devicesinclude: smartphones, tablets, personal computers, dedicated customgateways, audio devices, wearable activity monitors, automobileconsoles, other devices described herein this section or elsewhere inthe specification, and other available devices for passing data.

FIG. 13 is a schematic illustration of an embodiment of a system andmethod 3000 for a wearable ECG and/or medical sensor 3002 withtransmission capabilities, very similar to the system and/or methodsdescribed above in relation to FIGS. 11 and 12. FIG. 13 differs fromFIGS. 11 and 12 in that FIG. 13 illustrates alternate sensor channels3004, 3006 producing alternate outputs and/or extraction 3008 offeatures 3010. Collection of other channels of data may serve to furtheraugment ECG-extracted features. Data from the alternate sensor channelsmay be sent whole or specific features 3010 of the data channel may beextracted 3008. In certain embodiments, an alternate data channel mayrecord galvanic skin response/impedance. This data may indicate whetherthe sensor 3002 leads are on or off, for example, via a Booleanalgorithm indicating whether the leads are in an on/off state during agiven time period, based on a preset threshold and built-in hysteresis.This information could be further used to remove periods of non-contactof the device with the body from analysis. In certain embodiments, inaddition to the on/off indication Boolean, the collection of moregranular impedance data points may provide insight into the change inpatient activity levels, due to changes in sweat levels. In embodiments,an alternative sensor data channel may be from an accelerometer. Such adevice may provide free-fall detection via an on-board algorithm todetect free-fall, an indication that the patient may have suddenlyfallen due to arrhythmia-induced syncope. Further, the magnitude ofacceleration detected by the accelerometer may be used to detect periodsof sleep, activity levels, types of activity, and/or possibility ofmotion artifact, all of which may correlate with the prevalence ofcertain rhythm types. In particular embodiments, raw accelerometervalues may be used to determine body orientation given a reference point(for example, whether the patient was upright when they were firstpatched). Additionally, change in orientation of the accelerometer maybe used to distinguish clinically relevant morphology changes vs. notclinically relevant changes, in addition to providing more insight intoactivity type.

In some embodiments, an alternative data channel may be provided by apulse oximeter. For example, a photoplethysmogram (PPG) may be generatedby the pulse oximeter. The PPG may provide an alternative source forR-peak locations or as a cross-check on R-peak detection by the ECGcircuitry. Further, the PPG data channel may be combined with multiplePPG/BioZ channels to output confidence of R-peak detection confidencelevels. In further embodiments, SpO2/perfusion via the pulse oximetermay provide further clinical indications of a severe arrhythmia. Incertain embodiments, an alternative sensor channel may involvebioimpedance, which may be used to determine heart beat location and/oract as an alternative source for R-peak data. In some embodiments,temperature data may be provided via an alternative sensor channel. Thisdata can be used in conjunction with other metrics of activity todiscern activity type, level, and/or sleep. In some embodiments, thealternative data channel may provide information from a clock, forexample the time of day or an indication of daytime or nighttime. Incertain embodiments, the alternative data channel may be provided by amicrophone/stethoscope, providing an audible recording of heart beat.Lastly, an alternative data channel may be provided by a flex or bendsensor which may allow for identification of motion artifacts.

FIG. 14 is a schematic illustration of an embodiment of a system andmethod 4000 for a wearable ECG and/or medical sensor 4002 withtransmission capabilities, very similar to the system and/or methodsdescribed above in relation to FIGS. 11 to 13. FIG. 14 differs fromFIGS. 11 to 13 because the embodiment of FIG. 14 incorporates additionaldata filters. In some embodiments, the Processing Device 4004 may alsofilter rhythms 4006 identified by the rhythm inference system 4008 byapplying filter criteria that may derive from multiple sources. Forexample, filter criteria may be drawn from: physician interest in timelyreporting of particular rhythm types, physician interest in viewing arhythm similar to one that was viewed previously (for example ifmultiple 3-second pauses were already reported to the physician,changing the threshold of interest to 4- or 5-seconds). In certainembodiments, filtering may include automated filtering to limit repeatedretrieval of similar rhythm types and durations. Automated filtering canlimit repeated retrieval of low positive-predictivity events, forexample, a record with high levels of motion artifact where positivepredictivity of the rhythm inference engine was low, and may allowautomating filtering on subsequent requests. Such an approach mayutilize confidence intervals assigned by the Rhythm inference system aswell as the tracked history of rhythm inference system positivepredictivity for a given monitoring session.

FIG. 15 is a schematic illustration of an embodiment of a system 5000for a consumer wearable device without full ECG detection, with somesimilarities to the medical sensors of FIGS. 10 to 14. The sensors 5002need not be medical-grade ECG sensors, but merely allow detection ofbeats. In embodiments, the sensor 5002 may continuously sense a datachannel from which heart beat locations can be derived. Possible datasources include: PPG (optionally with multiple channels to increaseaccuracy), bio-impedence, and ECG without full implementation due toinsufficient signal quality as compared to the sensors of FIGS. 10 to14. Similar to the devices of FIGS. 10 to 14, features may be extractedfrom this signal, for example: R-peak locations, R-peak overflow flag,saturation flag, breathing rate, P/T wave locations, R-peak amplitude(or proxy), or ECG signal amplitude proxy. The data extraction may beperformed via any method described herein this section or elsewhere inthe specification. In certain embodiments, other channels of data arecollected to improve confidence in rhythm assessments. The consumerdevice system 5000 further transmits and processes data via any methoddescribed herein this section or elsewhere in the specification. Basedon these determinations, the results of the rhythm analysis may be sentto a user.

The consumer device system 5000 without full ECG sensing advantageouslyenables arrhythmia analysis using consumer-available heart-rate sensors,thereby reducing the cost and increasing the availability of the device.Consequently, this may enable arrhythmia screening on a largerpopulation, including via over-the-counter screening.

FIG. 16 is a schematic diagram of an embodiment of an ECG monitor system6000 with symptomatic transmission. Such a system would involve awearable ECG sensor, similar to the sensors described in relation toFIGS. 1 to 14. As described above, such a sensor senses and records ECGcontinuously. Each symptom trigger by a patient may initiate transfer ofan ECG data strip. The data strip may vary in temporal location as wellas duration, and may be centered around a triggered event. In certainembodiments, the data strip may be biased towards a time period prior tosymptom trigger or it may be biased towards time period after symptomtrigger. The data strip may be of a duration as short as a fewheartbeats (˜5-10 seconds), or of 60-90 second duration, or longer still(˜5-20 minutes). In embodiments, the data strip duration may bedynamically changed, either programmatically based on clinical need orauto-adjusting based on patient trigger frequency. The ECG data stripmay be transmitted via any of the means disclosed herein this section orelsewhere in the specification. If transmission is enabled without needfor patient intervention (for example not NFC or wired transmission),data transfer may initiate automatically and opportunistically withoutrequiring further patient interaction beyond symptom trigger.

The location for data strip analysis may vary. For example, analysis mayoccur local to the patient on a smartphone, tablet or PC. Alternatively,analysis may occur local to the clinic on a server or other processingdevice, or analysis may occur local to the ECG analysis service provideron a server or other processing device. Lastly, in embodiments, theanalysis may occur using cloud-based distributed processing resources.In certain embodiments, a report may be provided for each symptomaticECG data strip, however, a report may not be provided if the symptomaticECG data strip is not determined to be clinically interesting. In somecases, the report may be made available, but notification to the usermay be limited to those cases of particular clinical relevance. Offeringthis option can limit the demands on a user's time.

In certain embodiments, the report may be delivered in a variety ofways. For example, the report may be delivered: through a website,through a smartphone, tablet or PC application, through an ElectronicHealth Record (EMR/EHR) system with interoperability and integrationinto multiple providers' systems, or through automatic messaging such asemail, SMS, app-based messaging. The recipient of the report may vary,in some applications the report recipient may be the patient-user whilein other applications, the report recipient may be a clinician.

In particular embodiments, when monitoring is complete, the patientremoves the device and sends the complete continuous ECG record to adata processing location. The method of sending may vary, for example,it may be sent via physical transfer of the entire device, such as mailor bringing the device to the prescribing clinic or it may be sent sendvia local download of data and subsequent download to a data processinglocation. In some cases, the patient may not wait to remove the devicebefore sending a partial segment of the continuous ECG record, thiswould be enabled by transfer methods that do not require removal of thedevice, for example NFC or ultra-low-power wireless data transfer. Aswith symptomatic ECG analysis described above, the data processinglocation may vary.

FIG. 17 is a schematic diagram of an embodiment of an ECG monitor system7000 with both symptomatic and asymptomatic transmission. The wearablesensor is similar to the sensors described herein this section orelsewhere in the specification. However, in embodiments, eachasymptomatic trigger initiates transfer of an ECG data strip such asdescribed above. Further physical features of the physiologicalmonitoring device described herein this section or elsewhere in thespecification facilitate implementation as described. As with the otherembodiments described above, high-fidelity ECG recording, as enabled bythe designs detailed herein this section or elsewhere in thespecification, allows increased confidence in the accuracy of thefeature extraction.

Computing Systems and Methods

In some embodiments, the systems, tools and methods of using samedescribed above enable interactivity and data collection performed by acomputing system 13000. FIG. 18 is a block diagram showing an embodimentin which the computing system 13000 is in communication with a network13002 and various external computing systems 13004, such as a wearablesystem 13005, a gateway device 13006, which are also in communicationwith the network 13002. The computing system 13000 may be used toimplement systems and methods described herein. While the externalsystem 13004 are shown as grouped it is recognized that each of thesystems may be external from each other and/or remotely located.

In some embodiments, the computing system 13000 includes one or morecomputing devices, for example, a server, a laptop computer, a mobiledevice (for example, smart phone, smart watch, tablet, personal digitalassistant), a kiosk, automobile console, or a media player, for example.In one embodiment, the computing device 13000 includes one or morecentral processing units (CPUs) 13105, which may each include aconventional or proprietary microprocessor. The computing device 13000further includes one or more memory 13130, such as random access memory(RAM) for temporary storage of information, one or more read only memory(ROM) for permanent storage of information, and one or more mass storagedevice 13120, such as a hard drive, diskette, solid state drive, oroptical media storage device. In certain embodiments, the processingdevice, cloud server, server or gateway device, may be implemented as acomputing system 1300. In one embodiment, the modules of the computingsystem 13000 are connected to the computer using a standard based bussystem. In different embodiments, the standard based bus system could beimplemented in Peripheral Component Interconnect (PCI), Microchannel,Small Computer computing system Interface (SCSI), Industrial StandardArchitecture (ISA) and Extended ISA (EISA) architectures, for example.In addition, the functionality provided for in the components andmodules of computing device 13000 may be combined into fewer componentsand modules or further separated into additional components and modules.

The computing device 13000 may be controlled and coordinated byoperating system software, for example, iOS, Windows XP, Windows Vista,Windows 7, Windows 8, Windows 10, Windows Server, Embedded Windows,Unix, Linux, Ubuntu Linux, SunOS, Solaris, Blackberry OS, Android, orother operating systems. In Macintosh systems, the operating system maybe any available operating system, such as MAC OS X. In otherembodiments, the computing device 13000 may be controlled by aproprietary operating system. Conventional operating systems control andschedule computer processes for execution, perform memory management,provide file system, networking, I/O services, and provide a userinterface, such as a graphical user interface (GUI), among other things.

The exemplary computing device 13000 may include one or more I/Ointerfaces and devices 13110, for example, a touchpad or touchscreen,but could also include a keyboard, mouse, and printer. In oneembodiment, the I/O interfaces and devices 13110 include one or moredisplay devices (such as a touchscreen or monitor) that allow visualpresentation of data to a user. More particularly, a display device mayprovide for the presentation of GUIs, application software data, andmultimedia presentations, for example. The computing system 13000 mayalso include one or more multimedia devices 13140, such as cameras,speakers, video cards, graphics accelerators, and microphones, forexample.

The I/O interfaces and devices 13110, in one embodiment of the computingsystem and application tools, may provide a communication interface tovarious external devices. In one embodiment, the computing device 13000is electronically coupled to a network 13002, which comprises one ormore of a local area network, a wide area network, and/or the Internet,for example, via a wired, wireless, or combination of wired andwireless, communication link 13115. The network 13002 can communicatewith various sensors, computing devices, and/or other electronic devicesvia wired or wireless communication links.

In some embodiments, the filter criteria, signals and data are processedby rhythm inference module an application tool according to the methodsand systems described herein, may be provided to the computing system13000 over the network 13002 from one or more data sources 13010. Thedata sources may include one or more internal and/or external databases,data sources, and physical data stores. The data sources 13010, externalcomputing systems 13004 and the rhythm interface module 13190 mayinclude databases for storing data (for example, feature data, rawsignal data, patient data) according to the systems and methodsdescribed above, databases for storing data that has been processed (forexample, data to be transmitted to the sensor, data to be sent to theclinician) according to the systems and methods described above. In oneembodiment of FIG. 19, the sensor data 14050 may, in some embodiments,store data received from the sensor, received from the clinician, and soforth. The Rules Database 14060 may, in some embodiments, store data(for example, instructions, preferences, profile) that establishparameters for the thresholds for analyzing the feature data. In someembodiments, one or more of the databases or data sources may beimplemented using a relational database, such as Sybase, Oracle,CodeBase, MySQL, SQLite, and Microsoft® SQL Server, and other types ofdatabases such as, for example, a flat file database, anentity-relationship database, and object-oriented database, NoSQLdatabase, and/or a record-based database.

The computing system, in one embodiment, includes a rhythm interfacemodule 13190 that may be stored in the mass storage device 13120 asexecutable software codes that are executed by the CPU 13105. The rhythminterface module 13190 may have a Feature Module 14010, an AlternateData Module 14020, an Inference Module 14030, a Feedback Module 14040, aSensor Data Database 14050, and a Rules Database 14060. These modulesmay include by way of example, components, such as software components,object-oriented software components, subroutines, segments of programcode, drivers, firmware, microcode, circuitry, data, databases, datastructures, tables, arrays, and variables. These modules are alsoconfigured to perform the processes disclosed herein including, in someembodiments, the processes described with respect to FIGS. 10 to 17.

In general, the word “module,” as used herein, refers to logic embodiedin hardware or firmware, or to a collection of software instructions,possibly having entry and exit points, written in a programminglanguage, such as, for example, Python, Java, Lua, C and/or C++. Asoftware module may be compiled and linked into an executable program,installed in a dynamic link library, or may be written in an interpretedprogramming language such as, for example, BASIC, Perl, or Python. Itwill be appreciated that software modules may be callable from othermodules or from themselves, and/or may be invoked in response todetected events or interrupts. Software modules configured for executionon computing devices may be provided on a computer readable medium, suchas a compact disc, digital video disc, flash drive, or any othertangible medium. Such software code may be stored, partially or fully,on a memory device of the executing computing device, such as thecomputing system 13000, for execution by the computing device. Softwareinstructions may be embedded in firmware, such as an EPROM. It will befurther appreciated that hardware modules may be comprised of connectedlogic units, such as gates and flip-flops, and/or may be comprised ofprogrammable units, such as programmable gate arrays or processors. Theblock diagrams disclosed herein may be implemented as modules. Themodules described herein may be implemented as software modules, but maybe represented in hardware or firmware. Generally, the modules describedherein refer to logical modules that may be combined with other modulesor divided into sub-modules despite their physical organization orstorage.

Each of the processes, methods, and algorithms described in thepreceding sections may be embodied in, and fully or partially automatedby, code modules executed by one or more computer systems or computerprocessors comprising computer hardware. The code modules may be storedon any type of non-transitory computer-readable medium or computerstorage device, such as hard drives, solid state memory, optical disc,and/or the like. The systems and modules may also be transmitted asgenerated data signals (for example, as part of a carrier wave or otheranalog or digital propagated signal) on a variety of computer-readabletransmission mediums, including wireless-based and wired/cable-basedmediums, and may take a variety of forms (for example, as part of asingle or multiplexed analog signal, or as multiple discrete digitalpackets or frames). The processes and algorithms may be implementedpartially or wholly in application-specific circuitry. The results ofthe disclosed processes and process steps may be stored, persistently orotherwise, in any type of non-transitory computer storage such as, forexample, volatile or non-volatile storage.

The various features and processes described above may be usedindependently of one another, or may be combined in various ways. Allpossible combinations and subcombinations are intended to fall withinthe scope of this disclosure. In addition, certain method or processblocks may be omitted in some implementations. The methods and processesdescribed herein are also not limited to any particular sequence, andthe blocks or states relating thereto can be performed in othersequences that are appropriate. For example, described blocks or statesmay be performed in an order other than that specifically disclosed, ormultiple blocks or states may be combined in a single block or state.The example blocks or states may be performed in serial, in parallel, orin some other manner. Blocks or states may be added to or removed fromthe disclosed example embodiments. The example systems and componentsdescribed herein may be configured differently than described. Forexample, elements may be added to, removed from, or rearranged comparedto the disclosed example embodiments.

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements and/or steps. Thus, such conditional language is notgenerally intended to imply that features, elements and/or steps are inany way required for one or more embodiments or that one or moreembodiments necessarily include logic for deciding, with or without userinput or prompting, whether these features, elements and/or steps areincluded or are to be performed in any particular embodiment. The term“including” means “included but not limited to.” The term “or” means“and/or.”

Any process descriptions, elements, or blocks in the flow or blockdiagrams described herein and/or depicted in the attached figures shouldbe understood as potentially representing modules, segments, or portionsof code which include one or more executable instructions forimplementing specific logical functions or steps in the process.Alternate implementations are included within the scope of theembodiments described herein in which elements or functions may bedeleted, executed out of order from that shown or discussed, includingsubstantially concurrently or in reverse order, depending on thefunctionality involved, as would be understood by those skilled in theart.

All of the methods and processes described above may be at leastpartially embodied in, and partially or fully automated via, softwarecode modules executed by one or more computers. For example, the methodsdescribed herein may be performed by the computing system and/or anyother suitable computing device. The methods may be executed on thecomputing devices in response to execution of software instructions orother executable code read from a tangible computer readable medium. Atangible computer readable medium is a data storage device that canstore data that is readable by a computer system. Examples of computerreadable mediums include read-only memory, random-access memory, othervolatile or non-volatile memory devices, CD-ROMs, magnetic tape, flashdrives, and optical data storage devices.

It should be emphasized that many variations and modifications may bemade to the above-described embodiments, the elements of which are to beunderstood as being among other acceptable examples. All suchmodifications and variations are intended to be included herein withinthe scope of this disclosure. The foregoing description details certainembodiments. It will be appreciated, however, that no matter howdetailed the foregoing appears in text, the systems and methods can bepracticed in many ways. For example, a feature of one embodiment may beused with a feature in a different embodiment. As is also stated above,it should be noted that the use of particular terminology whendescribing certain features or aspects of the systems and methods shouldnot be taken to imply that the terminology is being re-defined herein tobe restricted to including any specific characteristics of the featuresor aspects of the systems and methods with which that terminology isassociated.

Various embodiments of a physiological monitoring device, methods, andsystems are disclosed herein. These various embodiments may be usedalone or in combination, and various changes to individual features ofthe embodiments may be altered, without departing from the scope of theinvention. For example, the order of various method steps may in someinstances be changed, and/or one or more optional features may be addedto or eliminated from a described device. Therefore, the description ofthe embodiments provided above should not be interpreted as undulylimiting the scope of the invention as it is set forth in the claims.

Various modifications to the implementations described in thisdisclosure may be made, and the generic principles defined herein may beapplied to other implementations without departing from the spirit orscope of this disclosure. Thus, the scope of the disclosure is notintended to be limited to the implementations shown herein, but are tobe accorded the widest scope consistent with this disclosure, theprinciples and the novel features disclosed herein.

Certain features that are described in this specification in the contextof separate embodiments also can be implemented in combination in asingle embodiment. Conversely, various features that are described inthe context of a single embodiment also can be implemented in multipleembodiments separately or in any suitable subcombination. Moreover,although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asubcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, such operations need not be performed in the particular ordershown or in sequential order, or that all illustrated operations beperformed, to achieve desirable results. Further, the drawings mayschematically depict one more example processes in the form of a flowdiagram. However, other operations that are not depicted can beincorporated in the example processes that are schematicallyillustrated. For example, one or more additional operations can beperformed before, after, simultaneously, or between any of theillustrated operations. Moreover, the separation of various systemcomponents in the embodiments described above should not be interpretedas requiring such separation in all embodiments. Additionally, otherembodiments are within the scope of the following claims. In some cases,the actions recited in the claims can be performed in a different orderand still achieve desirable results.

1-20. (canceled)
 21. A method of monitoring the physiological data of apatient using a wearable monitor, comprising: providing a wearablemonitor configured to contact the skin of the patient, the wearablemonitor comprising a hardware processer within an assembly, the assemblyconfigured to detect a cardiac signal; within the hardware processor,continuously collecting the cardiac signal from the patient, thehardware processor configured to use a machine-learned model to extracta machine-learned output from the cardiac signal or a derived signaltherefrom, the machine-learned output comprising less data than thecardiac signal; and wherein the wearable monitor is configured towirelessly transmit the machine-learned output to a computing system,the computing system configured to analyze the machine-learned output toinfer a likelihood of a cardiac arrhythmia originating in the past. 22.The method of claim 21, wherein transmitting the machine-learned outputconsumes less battery power than transmitting the cardiac signal. 23.The method of claim 21, wherein the cardiac signal comprises anelectrocardiogram signal.
 24. The method of claim 21, wherein thecardiac signal comprises a photoplethysmography signal.
 25. The methodof claim 21, wherein the wearable monitor comprises an electrode. 26.The method of claim 25, wherein the wearable monitor comprises aplurality of electrodes.
 27. The method of claim 21, wherein thewearable monitor comprises a chest strap.
 28. The method of claim 21,wherein the wearable monitor comprises a wrist-worn watch.
 29. Themethod of claim 21, wherein the wearable monitor comprises a housing anda plurality of wings extending from the housing, the wings configured toadhere to the skin of the patient.
 30. The method of claim 21, whereinthe computing system is remote from the wearable monitor.
 31. The methodof claim 21, further comprising collecting secondary physiological data.32. The method of claim 31, wherein the secondary physiological datacomprises motion data collected by an accelerometer.
 33. The method ofclaim 21, further comprising algorithmically comparing the secondaryphysiological data with the cardiac signal.
 34. The method of claim 21,wherein the secondary physiological data comprises electrode contactquality data.
 35. The method of claim 21, wherein the computing systemcomprises a gateway.
 36. The method of claim 21, further comprisinggenerating a report, the report comprising an indication of thelikelihood of an occurrence of cardiac arrhythmia.
 37. The method ofclaim 21, further comprising calculating atrial fibrillation burden. 38.The method of claim 37, wherein the atrial fibrillation burden comprisesan amount of time spent in atrial fibrillation by the patient.
 39. Themethod of claim 21, further comprising estimating a noise level from thecardiac signal.
 40. The method of claim 21, wherein the wearable monitoris configured to be worn for more than 7 days.